Chapter 1: Introduction to Expert Systems Expert Systems: Principles and Programming, Fourth Edition.

Chapter 1: Introduction to Expert Systems Expert Systems: Principles and Programming, Fourth Edition

2 Objectives Learn the meaning of an expert system Understand the problem domain and knowledge domain Learn the advantages of an expert system Understand the stages in the development of an expert system Examine the general characteristics of an expert system

Expert Systems: Principles and Programming, Fourth Edition 3 Objectives Examine earlier expert systems which have given rise to today’s knowledge-based systems Explore the applications of expert systems in use today Examine the structure of a rule-based expert system Learn the difference between procedural and nonprocedural paradigms What are the characteristics of artificial neural systems

Expert Systems: Principles and Programming, Fourth Edition 4 What is an expert system? “An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.” Professor Edward Feigenbaum Stanford University

Expert Systems: Principles and Programming, Fourth Edition 5 Fig 1.1 Areas of Artificial Intelligence

Expert Systems: Principles and Programming, Fourth Edition 6 Expert system technology may include: Special expert system languages – CLIPS Programs Hardware designed to facilitate the implementation of those systems

Expert Systems: Principles and Programming, Fourth Edition 7 Expert System Main Components Knowledge base – obtainable from books, magazines, knowledgeable persons, etc. Inference engine – draws conclusions from the knowledge base

Expert Systems: Principles and Programming, Fourth Edition 8 Figure 1.2 Basic Functions of Expert Systems

Expert Systems: Principles and Programming, Fourth Edition 9 Problem Domain vs. Knowledge Domain An expert’s knowledge is specific to one problem domain – medicine, finance, science, engineering, etc. The expert’s knowledge about solving specific problems is called the knowledge domain. The problem domain is always a superset of the knowledge domain.

Expert Systems: Principles and Programming, Fourth Edition 10 Figure 1.3 Problem and Knowledge Domain Relationship

Expert Systems: Principles and Programming, Fourth Edition 11 Advantages of Expert Systems Increased availability Reduced cost Reduced danger Performance Multiple expertise Increased reliability

Expert Systems: Principles and Programming, Fourth Edition 12 Advantages Continued Explanation Fast response Steady, unemotional, and complete responses at all times Intelligent tutor Intelligent database

Expert Systems: Principles and Programming, Fourth Edition 13 Representing the Knowledge The knowledge of an expert system can be represented in a number of ways, including IF- THEN rules: IF you are hungry THEN eat

Expert Systems: Principles and Programming, Fourth Edition 14 Knowledge Engineering The process of building an expert system: 1.The knowledge engineer establishes a dialog with the human expert to elicit knowledge. 2.The knowledge engineer codes the knowledge explicitly in the knowledge base. 3.The expert evaluates the expert system and gives a critique to the knowledge engineer.

Expert Systems: Principles and Programming, Fourth Edition 15 Development of an Expert System

Expert Systems: Principles and Programming, Fourth Edition 16 The Role of AI An algorithm is an ideal solution guaranteed to yield a solution in a finite amount of time. When an algorithm is not available or is insufficient, we rely on artificial intelligence (AI). Expert system relies on inference – we accept a “reasonable solution.”

Expert Systems: Principles and Programming, Fourth Edition 17 Uncertainty Both human experts and expert systems must be able to deal with uncertainty. It is easier to program expert systems with shallow knowledge than with deep knowledge. Shallow knowledge – based on empirical and heuristic knowledge. Deep knowledge – based on basic structure, function, and behavior of objects.

Expert Systems: Principles and Programming, Fourth Edition 18 Limitations of Expert Systems Typical expert systems cannot generalize through analogy to reason about new situations in the way people can. A knowledge acquisition bottleneck results from the time-consuming and labor intensive task of building an expert system.

Expert Systems: Principles and Programming, Fourth Edition 19 Early Expert Systems DENDRAL – used in chemical mass spectroscopy to identify chemical constituents MYCIN – medical diagnosis of illness DIPMETER – geological data analysis for oil PROSPECTOR – geological data analysis for minerals XCON/R1 – configuring computer systems

Expert Systems: Principles and Programming, Fourth Edition 20 Table 1.3 Broad Classes of Expert Systems

Expert Systems: Principles and Programming, Fourth Edition 21 Problems with Algorithmic Solutions Conventional computer programs generally solve problems having algorithmic solutions. Algorithmic languages include C, Java, and C#. Classic AI languages include LISP and PROLOG.

Expert Systems: Principles and Programming, Fourth Edition 22 Considerations for Building Expert Systems Can the problem be solved effectively by conventional programming? Is there a need and a desire for an expert system? Is there at least one human expert who is willing to cooperate? Can the expert explain the knowledge to the knowledge engineer can understand it. Is the problem-solving knowledge mainly heuristic and uncertain?

Expert Systems: Principles and Programming, Fourth Edition 23 Languages, Shells, and Tools Expert system languages are post-third generation. Procedural languages (e.g., C) focus on techniques to represent data. More modern languages (e.g., Java) focus on data abstraction. Expert system languages (e.g. CLIPS) focus on ways to represent knowledge.

Expert Systems: Principles and Programming, Fourth Edition 24 Expert systems Vs conventional programs I

Expert Systems: Principles and Programming, Fourth Edition 25 Expert systems Vs conventional programs II

Expert Systems: Principles and Programming, Fourth Edition 26 Expert systems Vs conventional programs III

Expert Systems: Principles and Programming, Fourth Edition 27 Elements of an Expert System User interface – mechanism by which user and system communicate. Exploration facility – explains reasoning of expert system to user. Working memory – global database of facts used by rules. Inference engine – makes inferences deciding which rules are satisfied and prioritizing.

Expert Systems: Principles and Programming, Fourth Edition 28 Elements Continued Agenda – a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory. Knowledge acquisition facility – automatic way for the user to enter knowledge in the system bypassing the explicit coding by knowledge engineer. Knowledge Base – includes the rules of the expert system

Expert Systems: Principles and Programming, Fourth Edition 29 Production Rules Knowledge base is also called production memory. Production rules can be expressed in IF-THEN pseudocode format. In rule-based systems, the inference engine determines which rule antecedents are satisfied by the facts.

Expert Systems: Principles and Programming, Fourth Edition 30 Figure 1.6 Structure of a Rule-Based Expert System

Expert Systems: Principles and Programming, Fourth Edition 31 Rule-Based ES

Expert Systems: Principles and Programming, Fourth Edition 32 Example Rules

Expert Systems: Principles and Programming, Fourth Edition 33 Inference Engine Cycle

Expert Systems: Principles and Programming, Fourth Edition 34 Foundation of Expert Systems

Expert Systems: Principles and Programming, Fourth Edition 35 General Methods of Inferencing Forward chaining (data-driven)– reasoning from facts to the conclusions resulting from those facts – best for prognosis, monitoring, and control. –Examples: CLIPS, OPS5 Backward chaining (query driven)– reasoning in reverse from a hypothesis, a potential conclusion to be proved to the facts that support the hypothesis – best for diagnosis problems. –Examples: MYCIN

Expert Systems: Principles and Programming, Fourth Edition 36 Production Systems Rule-based expert systems – most popular type today. Knowledge is represented as multiple rules that specify what should/not be concluded from different situations. Forward chaining – start w/facts and use rules do draw conclusions/take actions. Backward chaining – start w/hypothesis and look for rules that allow hypothesis to be proven true.

Expert Systems: Principles and Programming, Fourth Edition 37 Forward/Backward Chaining Forward chaining – primarily data-driven. Backward chaining – primarily goal driven.

Expert Systems: Principles and Programming, Fourth Edition 38 Post Production System Basic idea – any mathematical / logical system is simply a set of rules specifying how to change one string of symbols into another string of symbols. these rules are also known as rewrite rules simple syntactic string manipulation no understanding or interpretation is required\also used to define grammars of languages –e.g BNF grammars of programming languages. Basic limitation – lack of control mechanism to guide the application of the rules.

Expert Systems: Principles and Programming, Fourth Edition 39 Markov Algorithm An ordered group of productions applied in order or priority to an input string. If the highest priority rule is not applicable, we apply the next, and so on. An efficient algorithm for systems with many rules. Termination on (1) last production not applicable to a string, or (2) production ending with period applied Can be applied to substrings, beginning at left

Expert Systems: Principles and Programming, Fourth Edition 40 Markov Algorithm

Expert Systems: Principles and Programming, Fourth Edition 41 Rete Algorithm Markov: too inefficient to be used with many rules Functions like a net – holding a lot of information. Much faster response times and rule firings can occur compared to a large group of IF-THEN rules which would have to be checked one-by-one in conventional program. Takes advantage of temporal redundancy and structural similarity. Looks only for changes in matches (ignores static data) Drawback is high memory space requirements.

Expert Systems: Principles and Programming, Fourth Edition 42 Procedural Paradigms Algorithm – method of solving a problem in a finite number of steps. Procedural programs are also called sequential programs. The programmer specifies exactly how a problem solution must be coded.

Expert Systems: Principles and Programming, Fourth Edition 43 Figure 1.8 Procedural Languages

Expert Systems: Principles and Programming, Fourth Edition 44 Imperative Programming Also known as statement-oriented During execution, program makes transition from the initial state to the final state by passing through series of intermediate states. Provide rigid control and top-down-design. Not efficient for directly implementing expert systems.

Expert Systems: Principles and Programming, Fourth Edition 45 Functional Programming Function-based (association, domain, codomain); f: S  T Not much control Bottom-up  combine simple functions to yield more powerful functions. Mathematically a function is an association or rule that maps members of one set, the domain, into another set, the codomain. e.g. LISP and Prolog

Expert Systems: Principles and Programming, Fourth Edition 46 Nonprocedural Paradigms Do not depend on the programmer giving exact details how the program is to be solved. Declarative programming – goal is separated from the method to achieve it. Object-oriented programming – partly imperative and partly declarative – uses objects and methods that act on those objects. Inheritance – (OOP) subclasses derived from parent classes.

Expert Systems: Principles and Programming, Fourth Edition 47 Figure 1.9 Nonprocedural Languages

Expert Systems: Principles and Programming, Fourth Edition 48 What are Expert Systems? Can be considered declarative languages: Programmer does not specify how to achieve a goal at the algorithm level. Induction-based programming – the program learns by generalizing from a sample.

Expert Systems: Principles and Programming, Fourth Edition 49 Artificial Neural Systems In the 1980s, a new development in programming paradigms appeared called artificial neural systems (ANS). Based on the way the brain processes information. Models solutions by training simulated neurons connected in a network. ANS are found in face recognition, medical diagnosis, games, and speech recognition.

Expert Systems: Principles and Programming, Fourth Edition 50 ANS Characteristics A complex pattern recognition problem – computing the shortest route through a given list of cities. ANS is similar to an analog computer using simple processing elements connected in a highly parallel manner. Processing elements perform Boolean / arithmetic functions in the inputs Key feature is associating weights w/each element.

Expert Systems: Principles and Programming, Fourth Edition 51 Table 1.13 Traveling Salesman Problem

Expert Systems: Principles and Programming, Fourth Edition 52 Advantages of ANS Storage is fault tolerant Quality of stored image degrades gracefully in proportion to the amount of net removed. Nets can extrapolate (extend) and interpolate (insert/estimate) from their stored information. Nets have plasticity. Excellent when functionality is needed long-term w/o repair in hostile environment – low maintenance.

Expert Systems: Principles and Programming, Fourth Edition 53 Disadvantage of ANS ANS are not well suited for number crunching or problems requiring optimum solution.

Expert Systems: Principles and Programming, Fourth Edition 54 Figure 1.10 Neuron Processing Element

Expert Systems: Principles and Programming, Fourth Edition 55 Sigmoid Function

Expert Systems: Principles and Programming, Fourth Edition 56 Figure 1.11 A Back-Propagation Net

Expert Systems: Principles and Programming, Fourth Edition 57 Figure 1.12 Hopfield Artificial Neural Net

Expert Systems: Principles and Programming, Fourth Edition 58 MACIE An inference engine called MACIE (Matrix Controlled Inference Engine) uses ANS knowledge base. Designed to classify disease from symptoms into one of the known diseases the system has been trained on. MACIE uses forward chaining to make inferences and backward chaining to query user for additional data to reach conclusions.

Expert Systems: Principles and Programming, Fourth Edition 59 Summary During the 20 th Century various definitions of AI were proposed. In the 1960s, a special type of AI called expert systems dealt with complex problems in a narrow domain, e.g., medical disease diagnosis. Today, expert systems are used in a variety of fields. Expert systems solve problems for which there are no known algorithms.

Expert Systems: Principles and Programming, Fourth Edition 60 Summary Continued Expert systems are knowledge-based – effective for solving real-world problems. Expert systems are not suited for all applications. Future advances in expert systems will hinge on the new quantum computers and those with massive computational abilities in conjunction with computers on the Internet.

Chapter 2: The Representation of Knowledge

Objectives Introduce the study of logic Learn the difference between formal logic and informal logic Learn the meaning of knowledge and how it can be represented Learn about semantic nets Learn about object-attribute-value triples 62

Objectives Continued See how semantic nets can be translated into Prolog Explore the limitations of semantic nets Learn about schemas Learn about frames and their limitations Learn how to use logic and set symbols to represent knowledge 63

Objectives Continued Learn about propositional and first order predicate logic Learn about quantifiers Explore the limitations of propositional and predicate logic 64

What is the study of logic? Logic is the study of making inferences – given a set of facts, we attempt to reach a true conclusion. An example of informal logic is a courtroom setting where lawyers make a series of inferences hoping to convince a jury / judge. Formal logic (symbolic logic) is a more rigorous approach to proving a conclusion to be true / false. 65

Why is Logic Important We use logic in our everyday lives – “should I buy this car”, “should I seek medical attention”. People are not very good at reasoning because they often fail to separate word meanings with the reasoning process itself. Semantics refers to the meanings we give to symbols. 66

The Goal of Expert Systems We need to be able to separate the actual meanings of words with the reasoning process itself. We need to make inferences w/o relying on semantics. We need to reach valid conclusions based on facts only. 67

Knowledge in Expert Systems Knowledge representation is key to the success of expert systems. Expert systems are designed for knowledge representation based on rules of logic called inferences. Knowledge affects the development, efficiency, speed, and maintenance of the system. 68

Definitions of Knowledge a) (1) the fact or condition of knowing something with familiarity gained through experience or association (2)acquaintance with or understanding of a science, art, or technique b) (1) the fact or condition of being aware of something (2) the range of one's information or understanding c)the circumstance or condition of apprehending truth or fact through reasoning : cognition d)the fact or condition of having information or of being learned 69

Epistemology Epistemology is the formal study of knowledge. Concerned with nature, structure, and origins of knowledge. 70

Categories of Epistemology PhilosophyA priori A posterioriProcedural DeclarativeTacit 71

A Priori Knowledge Also called “theoretical knowledge” “That which precedes” Independent of the senses Universally true Cannot be denied without contradiction e.g., coin flips will give 50% heads and 50% tails 72

A Posteriori Knowledge Also called “empirical knowledge” “That which follows” Derived from the senses Now always reliable Deniable on the basis of new knowledge w/o the necessity of contradiction E.g., 100 coin flips give only 39 heads – what can you conclude? 73

Procedural Knowledge Knowing how to do something: Fix a watch Install a window Brush your teeth Ride a bicycle 74

Declarative Knowledge Knowledge that something is true or false Usually associated with declarative statements E.g., “Don’t touch that hot wire.” 75

Tacit Knowledge Unconscious knowledge Cannot be expressed by language E.g., knowing how to walk, breath, etc. 76

Knowledge in Rule-Based Systems Knowledge is part of a hierarchy. Knowledge refers to rules that are activated by facts or other rules. Activated rules produce new facts or conclusions. Conclusions are the end-product of inferences when done according to formal rules. 77

Knowledge in Rule-Based Systems II 78

Expert Systems vs. ANS ANS does not make inferences but searches for underlying patterns. Expert systems oDraw inferences using facts oSeparate data from noise oTransform data into information oTransform information into knowledge 79

Metaknowledge Metaknowledge is knowledge about knowledge and expertise. Most successful expert systems are restricted to as small a domain as possible. In an expert system, an ontology is the metaknowledge that describes everything known about the problem domain. Wisdom is the metaknowledge of determining the best goals of life and how to obtain them. 80

Figure 2.2 The Pyramid of Knowledge 81

Knowledge Representation Methods A number of knowledge-representation techniques have been devised: Production Rules Semantic nets Frames Scripts Logic Conceptual graphs 82

Production Rules Frequently used to formulate the knowledge in expert systems. A formal variation is Backus-Naur form (BNF) –metalanguage for the definition of language syntax –a grammar is a complete, unambiguous set of production rules for a specific language –a parse tree is a graphic representation of a sentence in that language –provides only a syntactic description of the language not all sentences make sense 83

Example: Production Rules 84

Example: Parse Tree of a Sentence 85

Advantages and Disadvantages of Production Rules Advantages: simple and easy to understand straightforward implementation formal foundations for some variants Disadvantages: simple implementations are very inefficient some types of knowledge are not easily expressed in such rules large sets of rules become difficult to understand and maintain 88

Semantic Nets A classic representation technique for propositional information (sometimes called propositional net) Propositions – a form of declarative knowledge, stating facts (true/false) Propositions are called “atoms” – cannot be further subdivided. Semantic nets consist of nodes (objects, concepts, situations) and arcs or links (relationships between them). For nodes –Labels indicate the name –Nodes can be instances (individual objects) or classes (generic nodes) 89

Links-Semantic Nets Links represent relationships –The relationships contain the structural information of the knowledge to be represented –The label indicates the type of the relationship Common types of links: –IS-A – relates an instance or individual to a generic class –A-KIND-OF – relates generic nodes to generic nodes 90

Semantic Net Example 92

Object-Attribute-Value Triple One problem with semantic nets is lack of standard definitions for link names (IS-A, AKO, etc.). The OAV triplet can be used to characterize all the knowledge in a semantic net. 98

Problems with Semantic Nets Disadvantages of semantic nets could be classified as: Expressiveness –no internal structure of nodes –relationships between multiple nodes –no easy way to represent heuristic information –extensions are possible, but cumbersome –best suited for binary relationships Efficiency –may result in large sets of nodes and links –search may lead to combinatorial explosion especially for queries with negative results Usability –lack of standards for link types –naming of nodes classes, instances 100

Schemata Knowledge Structure – an ordered collection of knowledge – not just data. Semantic Nets – are shallow knowledge structures – all knowledge is contained in nodes and links. Schema is a more complex knowledge structure than a semantic net. In a schema, a node is like a record which may contain data, records, and/or pointers to nodes. 108

Frames One type of schema is a frame (or script – time- ordered sequence of frames). Frames are useful for simulating commonsense knowledge. Semantic nets provide 2-dimensional knowledge; frames provide 3-dimensional. Frames represent related knowledge about narrow subjects having much default knowledge. 109

Frames (Cont.) A frame is a group of slots and fillers that defines a stereotypical object that is used to represent generic as well as specific knowledge. Commonsense knowledge is knowledge that is generally known. Prototypes are objects possessing all typical characteristics of whatever is being modeled. 110

Frames (Cont.) 111

Simple Frame Example 112

Figure 2.8 A Car Frame 113

Frame Structure 114

Slots 115

Usage of Frame 116

Restaurant Frame Example 117

Generic Restaurant Frame Generic RESTAURANT Frame Specialization-of: Business-Establishment Types: range: (Cafeteria, Fast-Food, Seat-Yourself, Wait-To-Be-Seated) default: Seat-Yourself if-needed: IF plastic-orange-counter THEN Fast-Food, IF stack-of-trays THEN Cafeteria, IF wait-for-waitress-sign or reservations-made THEN Wait-To-Be-Seated, OTHERWISE Seat-Yourself. Location: range: an ADDRESS if-needed: (Look at the MENU) Name: if-needed: (Look at the MENU) Food-Style: range: (Burgers, Chinese, American, Seafood, French) default: American if-added: (Update Alternatives of Restaurant) Times-of-Operation: range: a Time-of-Day default: open evenings except Mondays Payment-Form: range: (Cash, CreditCard, Check, Washing-Dishes-Script) Event-Sequence: default: Eat-at-Restaurant Script Alternatives: range: all restaurants with same Foodstyle if-needed: (Find all Restaurants with the same Foodstyle) [Rogers 1999] 118

Restaurant Script EAT-AT-RESTAURANT Script Props: (Restaurant, Money, Food, Menu, Tables, Chairs) Roles: (Hungry-Persons, Wait-Persons, Chef-Persons) Point-of-View: Hungry-Persons Time-of-Occurrence: (Times-of-Operation of Restaurant) Place-of-Occurrence: (Location of Restaurant) Event-Sequence: first: Enter-Restaurant Script then: if (Wait-To-Be-Seated-Sign or Reservations) then Get-Maitre-d's-Attention Script then: Please-Be-Seated Script then: Order-Food-Script then: Eat-Food-Script unless (Long-Wait) when Exit-Restaurant-Angry Script then: if (Food-Quality was better than Palatable) then Compliments-To-The-Chef Script then: Pay-For-It-Script finally: Leave-Restaurant Script [Rogers 1999] 119

Frame Advantages fairly intuitive for many applications –similar to human knowledge organization –suitable for causal knowledge –easier to understand than logic or rules very flexible 120

Frame Problems it is tempting to use frames as definitions of concepts –not appropriate because there may be valid instances of a concept that do not fit the stereotype –exceptions can be used to overcome this can get very messy inheritance –not all properties of a class stereotype should be propagated to subclasses –alteration of slots can have unintended consequences in subclasses 121

Logic and Sets Knowledge can also be represented by symbols of logic. Logic is the study of rules of exact reasoning – inferring conclusions from premises. Automated reasoning – logic programming in the context of expert systems. 122

Forms of Logic Earliest form of logic was based on the syllogism – developed by Aristotle. Syllogisms – have two premises that provide evidence to support a conclusion. Example: –Premise:All cats are climbers. –Premise:Garfield is a cat. –Conclusion:Garfield is a climber. 123

Venn Diagrams Venn diagrams can be used to represent knowledge. Universal set is the topic of discussion. Subsets, proper subsets, intersection, union, contained in, and complement are all familiar terms related to sets. An empty set (null set) has no elements. 124

Figure 2.13 Venn Diagrams 125

Propositional Logic Formal logic is concerned with syntax of statements, not semantics. Syllogism: All goons are loons. Zadok is a goon. Zadok is a loon. The words may be nonsense, but the form is correct – this is a “valid argument.” 126

Boolean vs. Aristotelian Logic Existential import – states that the subject of the argument must have existence. “All elves wear pointed shoes.” – not allowed under Aristotelian view since there are no elves. Boolean view relaxes this by permitting reasoning about empty sets. 127

Figure 2.14 Intersecting Sets 128

Boolean Logic Defines a set of axioms consisting of symbols to represent objects / classes. Defines a set of algebraic expressions to manipulate those symbols. Using axioms, theorems can be constructed. A theorem can be proved by showing how it is derived from a set of axioms. 129

Features of Propositional Logic Concerned with the subset of declarative sentences that can be classified as true or false. We call these sentences “statements” or “propositions”. Paradoxes – statements that cannot be classified as true or false. Open sentences – statements that cannot be answered absolutely. –Spinach tastes wonderful. (may or may not be true for a person) –He is tall. (he is a variable) 130

Features Continued Compound statements – formed by using logical connectives (e.g., AND, OR, NOT, conditional, and biconditional) on individual statements. Material implication – p  q states that if p is true, it must follow that q is true. Biconditional – p  q states that p implies q and q implies p. 131

Features Continued Tautology – a statement that is true for all possible cases. Contradiction – a statement that is false for all possible cases. Contingent statement – a statement that is neither a tautology nor a contradiction. 132

Truth Tables 133

Predicate Logic Compared to propositional logic, includes predicates as well as Universal and Existential Quantifiers. More details in the next chapter! 134

Universal Quantifier The universal quantifier, represented by the symbol means “for every” or “for all”. ( x) (x is a rectangle  x has four sides) The existential quantifier, represented by the symbol means “there exists”. ( x) (x – 3 = 5) Limitations of predicate logic – most quantifier. 135

Figure 2.6: General Organization of a PROLOG System 136

PROLOG and Semantic Nets In PROLOG, predicate expressions consist of the predicate name, followed by zero or more arguments enclosed in parentheses, separated by commas. Example: mother(becky,heather) means that becky is the mother of heather 137

PROLOG Continued Programs consist of facts and rules in the general form of goals. General form: p:- p1, p2, …, pN p is called the rule’s head and the p i represents the subgoals Example: spouse(x,y) :- wife(x,y) x is the spouse of y if x is the wife of y 138

Chapter 3: Methods of Inference

Objectives Learn the definitions of trees, lattices, and graphs Learn about state and problem spaces Learn about AND-OR trees and goals Explore different methods and rules of inference Learn the characteristics of first-order predicate logic and logic systems 140

Objectives Discuss the resolution rule of inference, resolution systems, and deduction Compare shallow and causal reasoning How to apply resolution to first-order predicate logic Learn the meaning of forward and backward chaining 141

Objectives Explore additional methods of inference Learn the meaning of Metaknowledge Explore the Markov decision process 142

Trees A tree is a hierarchical data structure consisting of: –Nodes – store information –Branches – connect the nodes The top node is the root, occupying the highest hierarchy. The leaves are at the bottom, occupying the lowest hierarcy. 143

Trees Every node, except the root, has exactly one parent. Every node may give rise to zero or more child nodes. A binary tree restricts the number of children per node to a maximum of two. Degenerate trees have only a single pathway from root to its one leaf. 144

Figure 3.1 Binary Tree 145

Graphs Graphs are sometimes called a network or net. A graph can have zero or more links between nodes – there is no distinction between parent and child. Sometimes links have weights – weighted graph; or, arrows – directed graph. Simple graphs have no loops – links that come back onto the node itself. 146

Graphs A circuit (cycle) is a path through the graph beginning and ending with the same node. Acyclic graphs have no cycles. Connected graphs have links to all the nodes. Digraphs are graphs with directed links. Lattice is a directed acyclic graph. A Degenerate tree is a tree with only a single path from the root to its one leaf. 147

Figure 3.2 Simple Graphs 148

Making Decisions Trees / lattices are useful for classifying objects in a hierarchical nature. Trees / lattices are useful for making decisions. We refer to trees / lattices as structures. Decision trees are useful for representing and reasoning about knowledge. 149

Binary Decision Trees Every question takes us down one level in the tree. A binary decision tree having N nodes: –All leaves will be answers. –All internal nodes are questions. –There will be a maximum of 2 N answers for N questions. Decision trees can be self learning. Decision trees can be translated into production rules. 150

Decision Tree Example 151

State and Problem Spaces A state space can be used to define an object’s behavior. Different states refer to characteristics that define the status of the object. A state space shows the transitions an object can make in going from one state to another. 152

Finite State Machine A FSM is a diagram describing the finite number of states of a machine. At any one time, the machine is in one particular state. The machine accepts input and progresses to the next state. FSMs are often used in compilers and validity checking programs. 153

Using FSM to Solve Problems Characterizing ill-structured problems – one having uncertainties. Well-formed problems: – Explicit problem, goal, and operations are known –Deterministic – we are sure of the next state when an operator is applied to a state. –The problem space is bounded. –The states are discrete. 154

Figure 3.5 State Diagram for a Soft Drink Vending Machine Accepting Quarters (Q) and Nickels (N) 155

AND-OR Trees and Goals 1990s, PROLOG was used for commercial applications in business and industry. PROLOG uses backward chaining to divide problems into smaller problems and then solves them. AND-OR trees also use backward chaining. AND-OR-NOT lattices use logic gates to describe problems. 157

Types of Logic Deduction – reasoning where conclusions must follow from premises (general to specific) Induction – inference is from the specific case to the general Intuition – no proven theory-Recognizing a pattern(unconsciously)  ANN Heuristics – rules of thumb based on experience Generate and test – trial and error – often used to reach efficiency. 160

Types of Logic Abduction – reasoning back from a true condition to the premises that may have caused the condition Default – absence of specific knowledge Autoepistemic – self-knowledge…The color of the sky as it appears to you. Nonmonotonic – New evidence may invalidate previous knowledge Analogy – inferring conclusions based on similarities with other situations  ANN Commonsense knowledge – A combination of all based on our experience 161

Deductive Logic Argument – group of statements where the last is justified on the basis of the previous ones Deductive logic can determine the validity of an argument. Syllogism – has two premises and one conclusion Deductive argument – conclusions reached by following true premises must themselves be true 162

Syllogisms vs. Rules Syllogism: –All basketball players are tall. –Jason is a basketball player.  Jason is tall. IF-THEN rule: IF All basketball players are tall and Jason is a basketball player THEN Jason is tall. 163

Categorical Syllogism Premises and conclusions are defined using categorical statements of the form: 164

Categorical Syllogisms 165

Categorical Syllogisms 166

Proving the Validity of Syllogistic Arguments Using Venn Diagrams 1.If a class is empty, it is shaded. 2.Universal statements, A and E are always drawn before particular ones. 3.If a class has at least one member, mark it with an *. 4.If a statement does not specify in which of two adjacent classes an object exists, place an * on the line between the classes. 5.If an area has been shaded, no * can be put in it.  Review pages 131 to 135 167

Proving the Validity of Syllogistic Arguments Using Venn Diagrams 168  Invalid  Valid

Proving the Validity of Syllogistic Arguments Using Venn Diagrams 169  Valid

Rules of Inference Venn diagrams are insufficient for complex arguments. Syllogisms address only a small portion of the possible logical statements. Propositional logic offers another means of describing arguments  Variables 170

Direct Reasoning Modus Ponens 171

Truth Table Modus Ponens 172

Some Rules of Inference 173 (Modus Ponens)

Rules of Inference 174

The Modus Meanings 175

The Conditional and Its Variants 176

Requirements of a Formal System 1.An alphabet of symbols 2.A set of finite strings of these symbols, the wffs. 3.Axioms, the definitions of the system. 4.Rules of inference, which enable a wff to be deduced as the conclusion of a finite set of other wffs – axioms or other theorems of the logic system. 177

Requirements of a FS Continued 5.Completeness – every wff can either be proved or refuted. 6.The system must be sound – every theorem is a logically valid wff. 178

Logic Systems A logic system consists of four parts: Alphabet: a set of basic symbols from which more complex sentences are made. Syntax: a set of rules or operators for constructing expressions (sentences). Semantics: for defining the meaning of the sentences Inference rules: for constructing semantically equivalent but syntactically different sentences 179

WFF and Wang’s Propositional Theorem Proofer Well Formed Formula for Propositional Calculus Wang’s Propositional Theorem Proofer Handouts are provided in the class 180

Predicate Logic Syllogistic logic can be completely described by predicate logic. The Rule of Universal Instantiation states that an individual may be substituted for a universe. Compared to propositional logic, it has predicates, universal and existential quantifies. 181

Predicate Logic The following types of symbols are allowed in predicate logic: Terms Predicates Connectives Quantifiers 182

Predicate Logic Terms: Constant symbols: symbols, expressions, or entities which do not change during execution (e.g., true / false) Variable symbols: represent entities that can change during execution Function symbols: represent functions which process input values on a predefined list of parameters and obtain resulting values 183

Predicate Logic Predicates: Predicate symbols: represent true/false-type relations between objects. Objects are represented by constant symbols. 184

Predicate Logic Connectives: Conjunction Disjunction Negation Implication Equivalence … (same as for propositional calculus) 185

Predicate Logic Quantifiers: valid for variable symbols Existential quantifier: “There exists at least one value for x from its domain.” Universal quantifier: “For all x in its domain.” 186

First-Order Logic First-order logic allows quantified variables to refer to objects, but not to predicates or functions. For applying an inference to a set of predicate expressions, the system has to process matches of expressions. The process of matching is called unification. 187

PROLOG Programming in Logic 188

PROLOG: Horn Clauses 189

PROLOG: Facts 190

Knowledge Representation 191

PROLOG: Architecture 192

Family Example: Facts 193

Family Example: Rules 194

PROLOG Sample Dialogue 195

PROLOG Sample Inference 196

PROLOG Sample Inference 197

Shallow and Causal Reasoning Experiential knowledge is based on experience. In shallow reasoning, there is little/no causal chain of cause and effect from one rule to another. Advantage of shallow reasoning is ease of programming. Frames are used for causal / deep reasoning. Causal reasoning can be used to construct a model that behaves like the real system. 198

Chaining Chain – a group of multiple inferences that connect a problem with its solution A chain that is searched / traversed from a problem to its solution is called a forward chain. A chain traversed from a hypothesis back to the facts that support the hypothesis is a backward chain. Problem with backward chaining is find a chain linking the evidence to the hypothesis. 199

Causal Forward Chaining 200

Backward Chaining 201

Some Characteristics of Forward and Backward Chaining 202

Figure 3.14 Types of Inference 203

Metaknowledge The Markov decision process (MDP) is a good application to path planning. In the real world, there is always uncertainty, and pure logic is not a good guide when there is uncertainty. A MDP is more realistic in the cases where there is partial or hidden information about the state and parameters, and the need for planning. 204

Chapter 4: Reasoning Under Uncertainty

Objectives Learn the meaning of uncertainty and explore some theories designed to deal with it Find out what types of errors can be attributed to uncertainty and induction Learn about classical probability, experimental, and subjective probability, and conditional probability Explore hypothetical reasoning and backward induction 206 2/29/2016

Objectives Examine temporal reasoning and Markov chains Define odds of belief, sufficiency, and necessity Determine the role of uncertainty in inference chains Explore the implications of combining evidence Look at the role of inference nets in expert systems and see how probabilities are propagated 207 2/29/2016

How to Expert Systems Deal with Uncertainty? Expert systems provide an advantage when dealing with uncertainty as compared to decision trees. With decision trees, all the facts must be known to arrive at an outcome. Probability theory is devoted to dealing with theories of uncertainty. There are many theories of probability – each with advantages and disadvantages. 208 2/29/2016

What is Uncertainty? Uncertainty is essentially lack of information to formulate a decision. Uncertainty may result in making poor or bad decisions. As living creatures, we are accustomed to dealing with uncertainty – that’s how we survive. Dealing with uncertainty requires reasoning under uncertainty along with possessing a lot of common sense. 209 2/29/2016

Dealing with Uncertainty Deductive reasoning – deals with exact facts and exact conclusions Inductive reasoning – not as strong as deductive – premises support the conclusion but do not guarantee it. There are a number of methods to pick the best solution in light of uncertainty. When dealing with uncertainty, we may have to settle for just a good solution. 210 2/29/2016

Theories to Deal with Uncertainty Bayesian Probability Hartley Theory Shannon Theory Dempster-Shafer Theory Markov Models Zadeh’s Fuzzy Theory 211 2/29/2016

Errors Related to Hypothesis Many types of errors contribute to uncertainty. –Type I Error – accepting a hypothesis when it is not true – False Positive. –Type II Error – Rejecting a hypothesis when it is true – False Negative 212 2/29/2016

Errors Related to Measurement Errors of precision – how well the truth is known Errors of accuracy – whether something is true or not Unreliability stems from faulty measurement of data – results in erratic data. Random fluctuations – termed random error Systematic errors result from bias 213 2/29/2016

Errors in Induction Where deduction proceeds from general to specific, induction proceeds from specific to general. Inductive arguments can never be proven correct (except in mathematical induction). Expert systems may consist of both deductive and inductive rules based on heuristic information. When rules are based on heuristics, there will be uncertainty. 214 2/29/2016

Deductive and Inductive Reasoning about Populations and Samples 215 2/29/2016

Types of Errors 216 2/29/2016

Examples of Common Types of Errors 217 2/29/2016

Classical Probability First proposed by Pascal and Fermat in 1654 Also called a priori probability because it deals with ideal games or systems: –Assumes all possible events are known –Each event is equally likely to happen Fundamental theorem for classical probability is P = W / N, where W is the number of wins and N is the number of equally possible events. 218 2/29/2016

Deterministic vs. Nondeterministic Systems When repeated trials give the exact same results, the system is deterministic. Otherwise, the system is nondeterministic. Nondeterministic does not necessarily mean random – could just be more than one way to meet one of the goals given the same input. 219 2/29/2016

Three Axioms of Formal Theory of Probability 220 If E1 and E2 are mutually exclusive events 2/29/2016

Experimental and Subjective Probabilities Experimental probability defines the probability of an event, as the limit of a frequency distribution: Experimental probability is also called a posteriori (after the event) Subjective probability deals with events that are not reproducible and have no historical basis on which to extrapolate. 221

Compound Probabilities Compound probabilities can be expressed by: S is the sample space and A and B are events. Independent events are events that do not affect each other. For pairwise independent events, 222

Additive Law 223 2/29/2016

Conditional Probabilities The probability of an event A occurring, given that event B has already occurred is called conditional probability: 224

Sample Space of Intersecting Events 225 2/29/2016

Advantages and Disadvantages of Probabilities 226 2/29/2016 Advantages: –formal foundation –reflection of reality (posteriori) Disadvantages: –may be inappropriate the future is not always similar to the past –inexact or incorrect especially for subjective probabilities –Ignorance probabilities must be assigned even if no information is available –assigns an equal amount of probability to all such items –non-local reasoning requires the consideration of all available evidence, not only from the rules currently under consideration –no compositionality complex statements with conditional dependencies can not be decomposed into independent parts

Bayes’ Theorem This is the inverse of conditional probability. Find the probability of an earlier event given that a later one occurred. 227

Hypothetical Reasoning Backward Induction Bayes’ Theorem is commonly used for decision tree analysis of business and social sciences. especially useful in diagnostic systems medicine, computer help systems inverse or a posteriori probability inverse to conditional probability of an earlier event given that a later one occurred PROSPECTOR (expert system) achieved great fame as the first expert system to discover a valuable molybdenum deposit worth $100,000,000. 228 2/29/2016

Advantages and Disadvantages of Bayesian Reasoning 230 Advantages: sound theoretical foundation well-defined semantics for decision making Disadvantages: requires large amounts of probability data subjective evidence may not be reliable independence of evidences assumption often not valid relationship between hypothesis and evidence is reduced to a number explanations for the user difficult high computational overhead –Major problem: the relationship between belief and disbelief P (H | E) = 1 - P (H' | E) may not always work!

Temporal Reasoning Reasoning about events that depend on time Expert systems designed to do temporal reasoning to explore multiple hypotheses in real time are difficult to build. One approach to temporal reasoning is with probabilities – a system moving from one state to another over time. The process is stochastic if it is probabilistic. 231 2/29/2016

Markov Chain Process Transition matrix – represents the probabilities that the system in one state will move to another. State matrix – depicts the probabilities that the system is in any certain state. One can show whether the states converge on a matrix called the steady-state matrix – a time of equilibrium 232 2/29/2016

Markov Chain Characteristics 1.The process has a finite number of possible states. 2.The process can be in one and only one state at any one time. 3.The process moves or steps successively from one state to another over time. 4.The probability of a move depends only on the immediately preceding state. 233 2/29/2016

State Diagram Interpretation of a Transition Matrix 234 2/29/2016

The Odds of Belief To make expert systems work for use, we must expand the scope of events to deal with propositions. Rather than interpreting conditional probabilities P(A|B) in the classical sense, we interpret it to mean the degree of belief that A is true, given B. We talk about the likelihood of A, based on some evidence B. This can be interpreted in terms of odds. 235 2/29/2016

The Odds of Belief (cont.) The conditional probability could be referred to as the likelihood. Likelihood could be interpreted in terms of odds of a bet. The odds of A against B given some event C is: 236 2/29/2016

The Odds of Belief (cont.) The likelihood of P=95% (for the car to start in the morning) is thus equivalent to: Probability is natural forward chaining or deductive while likelihood is backward chaining and inductive. Although we use the same simbol, P(A|B), for probability and likelihood the applications are different. 237 2/29/2016

Sufficiency and Necessity The likelihood of sufficiency, LS, is calculated as: The likelihood of necessity is defined as: 238

Relationship Among Likelihood Ratio, Hypothesis, and Evidence 239 2/29/2016

Relationship Among Likelihood of Necessity, Hypothesis, and Evidence 240 2/29/2016

Uncertainty in Inference Chains Uncertainty may be present in rules, evidence used by rules, or both. One way of correcting uncertainty is to assume that P(H|e) is a piecewise linear function. 241 2/29/2016

Intersection of H and e 242 2/29/2016

Piecewise Linear Interpolation Function for Partial Evidence in PROSPECTOR 243 2/29/2016

Combination of Evidence The simplest type of rule is of the form: –IF E THEN H where E is a single piece of known evidence from which we can conclude that H is true. Not all rules may be this simple – compensation for uncertainty may be necessary. As the number of pieces of evidence increases, it becomes impossible to determine all the joint and prior probabilities or likelihoods. 244 2/29/2016

Combination of Evidence Continued If the antecedent is a logical combination of evidence, then fuzzy logic and negation rules can be used to combine evidence. 245 2/29/2016

Types of Belief Possible – no matter how remote, the hypothesis cannot be ruled out. Probable – there is some evidence favoring the hypothesis but not enough to prove it. Certain – evidence is logically true or false. Impossible – it is false. Plausible – more than a possibility exists. 246 2/29/2016

Figure 4.20 Relative Meaning of Some Terms Used to Describe Evidence 247 2/29/2016

Propagation of Probabilities The chapter examines the classic expert system PROSPECTOR to illustrate how concepts of probability are used in a real system. Inference nets like PROSPECTOR have a static knowledge structure. Common rule-based system is a dynamic knowledge structure. 248 2/29/2016

Summary In this chapter, we began by discussing reasoning under uncertainty and the types of errors caused by uncertainty. Classical, experimental, and subjective probabilities were discussed. Methods of combining probabilities and Bayes’ theorem were examined. PROSPECTOR was examined in detail to see how probability concepts were used in a real system. 249 2/29/2016

Summary An expert system must be designed to fit the real world, not visa versa. Theories of uncertainty are based on axioms; often we don’t know the correct axioms – hence we must introduce extra factors, fuzzy logic, etc. We looked at different degrees of belief which are important when interviewing an expert. 250 2/29/2016

Chapter 5: Inexact Reasoning

Objectives Explore the sources of uncertainty in rules Analyze some methods for dealing with uncertainty Learn about the Dempster-Shafer theory Learn about the theory of uncertainty based on fuzzy logic Discuss some commercial applications of fuzzy logic 252 4/4/2011

Uncertainty and Rules We have already seen that expert systems can operate within the realm of uncertainty. There are several sources of uncertainty in rules: –Uncertainty related to individual rules –Uncertainty due to conflict resolution –Uncertainty due to incompatibility of rules 253 4/4/2011

Figure 5.1 Major Uncertainties in Rule-Based Expert Systems 254 4/4/2011

Figure 5.2 Uncertainties in Individual Rules 255 4/4/2011

Figure 5.3 Uncertainty Associated with the Compatibilities of Rules 256 4/4/2011

Figure 5.4 Uncertainty Associated with Conflict Resolution 257 4/4/2011

Goal of Knowledge Engineer The knowledge engineer endeavors to minimize, or eliminate, uncertainty if possible. Minimizing uncertainty is part of the verification of rules. Verification is concerned with the correctness of the system’s building blocks – rules. 258 4/4/2011

Verification vs. Validation Even if all the rules are correct, it does not necessarily mean that the system will give the correct answer. Verification refers to minimizing the local uncertainties. Validation refers to minimizing the global uncertainties of the entire expert system. Uncertainties are associated with creation of rules and also with assignment of values. 259 4/4/2011

Ad Hoc Methods The ad hoc introduction of formulas such as fuzzy logic to a probabilistic system introduces a problem. The expert system lacks the sound theoretical foundation based on classical probability. The danger of ad hoc methods is the lack of complete theory to guide the application or warn of inappropriate situations. 260 4/4/2011

Sources of Uncertainty Potential contradiction of rules – the rules may fire with contradictory consequents, possibly as a result of antecedents not being specified properly. Subsumption of rules – one rules is subsumed by another if a portion of its antecedent is a subset of another rule. 261 4/4/2011

Uncertainty in Conflict Resolution There is uncertainty in conflict resolution with regard to priority of firing and may depend on a number of factors, including: Explicit priority rules Implicit priority of rules Specificity of patterns Recency of facts matching patterns Ordering of patterns Lexicographic Means-Ends Analysis Ordering that rules are entered 262 4/4/2011

Uncertainty When a fact is entered in the working memory, it receives a unique timetag – indicating when it was entered. The order that rules are entered may be a factor in conflict resolution – if the inference engine cannot prioritize rules, arbitrary choices must be made. Redundant rules are accidentally entered / occur when a rule is modified by pattern deletion. 263 4/4/2011

Uncertainty Deciding which redundant rule to delete is not a trivial matter. Uncertainty arising from missing rules occurs if the human expert forgets or is unaware of a rule. Data fusion is another cause of uncertainty – fusing of data from different types of information. 264 4/4/2011

Certainty Factors Another method of dealing with uncertainty uses certainty factors, originally developed for the MYCIN expert system. 265 4/4/2011

Difficulties with Bayesian Method The Bayesian method is useful in medicine / geology because we are determining the probability of a specific event (disease / location of mineral deposit), given certain symptoms / analyses. The problem is with the difficulty / impossibility of determining the probabilities of these givens – symptoms / analyses. Evidence tends to accumulate over time. 266 4/4/2011

Belief and Disbelief Consider the statement: “The probability that I have a disease plus the probability that I do not have the disease equals one.” Now, consider an alternate form of the statement: “The probability that I have a disease is one minus the probability that I don’t have it.” 267 4/4/2011

Belief and Disbelief It was found that physicians were reluctant to state their knowledge in the form: “The probability that I have a disease is one minus the probability that I don’t have it.” Symbolically, P(H|E) = 1 – P(H’|E), where E represents evidence 268 4/4/2011

Likelihood of Belief / Disbelief The reluctance by the physicians stems from the likelihood of belief / disbelief – not in the probabilities. The equation, P(H|E) = 1 – P(H’|E), implies a cause-and-effect relationship between E and H. The equation implies a cause-and-effect relationship between E and H’ if there is a cause- and-effect between E and H. 269 4/4/2011

Measures of Belief and Disbelief measure of belief –degree to which hypothesis H is supported by evidence E –MB(H,E) = 1 if P(H) =1 (P(H|E) - P(H)) / (1- P(H)) otherwise measure of disbelief –degree to which doubt in hypothesis H is supported by evidence E –MB(H,E) = 1 if P(H) =0 (P(H) - P(H|E)) / P(H)) otherwise 270 4/4/2011

Certainty Factor The certainty factor, CF, is a way of combining belief and disbelief into a single number. This has two uses: 1.The certainty factor can be used to rank hypotheses in order of importance. 2.The certainty factor indicates the net belief in a hypothesis based on some evidence. 271 4/4/2011

Certainty Factor certainty factor CF –ranges between -1 (denial of the hypothesis H) and +1 (confirmation of H) –allows the ranking of hypotheses difference between belief and disbelief CF (H,E) = MB(H,E) - MD (H,E) combining antecedent evidence –use of premises with less than absolute confidence –E1 ∧ E2 = min(CF(H, E1), CF(H, E2)) –E1 ∨ E2 = max(CF(H, E1), CF(H, E2)) – ￢ E = ￢ CF(H, E) 272 4/4/2011

Certainty Factor Values Positive CF – evidence supports the hypothesis CF = 1 – evidence definitely proves the hypothesis CF = 0 – there is no evidence or the belief and disbelief completely cancel each other. Negative CF – evidence favors negation of the hypothesis – more reason to disbelieve the hypothesis than believe it 273 4/4/2011

Combining Certainty Factors certainty factors that support the same conclusion several rules can lead to the same conclusion applied incrementally as new evidence becomes available CFc(CF1, CF2) = CF1 + CF 2(1 - CF1) if both > 0 CF1 + CF 2(1 + CF1) if both < 0 CF1 + CF2 / (1 - min(|CF1|, |CF2|)) if one < 0 274 4/4/2011

Characteristics of Certainty Factors 275 4/4/2011 Ranges measure of belief 0 ≤ MB ≤ 1 measure of disbelief 0 ≤ MD ≤ 1 certainty factor -1 ≤ CF ≤ +1

Threshold Values In MYCIN, a rule’s antecedent CF must be greater than 0.2 for the antecedent to be considered true and activate the rule. This threshold value minimizes the activation of rules that only weakly suggest the hypothesis. This improves efficiency of the system – preventing rules to be activated with little or no value. A combining function can be used. 276 4/4/2011

Difficulties with Certainty Factors In MYCIN, which was very successful in diagnosis, there were difficulties with theoretical foundations of certain factors. There was some basis for the CF values in probability theory and confirmation theory, but the CF values were partly ad hoc. Also, the CF values could be the opposite of conditional probabilities. 277 4/4/2011

Dempster-Shafer Theory The Dempster-Shafer Theory is a method of inexact reasoning. It is based on the work of Dempster who attempted to model uncertainty by a range of probabilities rather than a single probabilistic number. 278 4/4/2011

Dempster-Shafer 1.The Dempster-Shafer theory assumes that there is a fixed set of mutually exclusive and exhaustive elements called environment and symbolized by the Greek letter  :  = {  1,  2, …,  N } 279 4/4/2011

Dempster-Shafer The environment is another term for the universe of discourse in set theory. Consider the following: 2= {rowboat, sailboat, destroyer, aircraft carrier} These are all mutually exclusive elements 280 4/4/2011

Dempster-Shafer Consider the question: “What are the military boats?” The answer would be a subset of  : {  3,  4 } = {destroyer, aircraft carrier} 281 4/4/2011

Dempster-Shafer Consider the question: “What boat is powered by oars?” The answer would also be a subset of  : {  1 } = {rowboat} This set is called a singleton because it contains only one element. 282 4/4/2011

Dempster-Shafer Each of these subsets of  is a possible answer to the question, but there can be only one correct answer. Consider each subset an implied proposition: –The correct answer is: {  1,  2,  3 ) –The correct answer is: {  1,  3} All subsets of the environment can be drawn as a hierarchical lattice with  at the top and the null set  at the bottom 283 4/4/2011

Dempster-Shafer An environment is called a frame of discernment when its elements may be interpreted as possible answers and only one answer is correct. If the answer is not in the frame, the frame must be enlarged to accommodate the additional knowledge of element.. 284 4/4/2011

Dempster-Shafer 2.Mass Functions and Ignorance In Bayesian theory, the posterior probability changes as evidence is acquired. In Dempster- Shafer theory, the belief in evidence may vary. We talk about the degree of belief in evidence as analogous to the mass of a physical object – evidence measures the amount of mass. 285 4/4/2011

Dempster-Shafer Dempster-Shafer does not force belief to be assigned to ignorance – any belief not assigned to a subset is considered no belief (or non-belief) and just associated with the environment. Every set in the power set of the environment which has mass > 0 is a focal element. Every mass can be thought of as a function: m: P (  )  [0, 1] 286 4/4/2011

Dempster-Shafer 3.Combining Evidence Dempster’s rule combines mass to produce a new mass that represents the consensus of the original, possibly conflicting evidence The lower bound is called the support; the upper bound is called the plausibility; the belief measure is the total belief of a set and all its subsets. 287 4/4/2011

Dempster-Shafer 4.The moving mass analogy is helpful to understanding the support and plausibility. –The support is the mass assigned to a set and all its subsets –Mass of a set can move freely into its subsets –Mass in a set cannot move into its supersets –Moving mass from a set into its subsets can only contribute to the plausibility of the subset, not its support. –Mass in the environment can move into any subset. 288 4/4/2011

Approximate Reasoning This is theory of uncertainty based on fuzzy logic and concerned with quantifying and reasoning using natural language where words have ambiguous meaning. Fuzzy logic is a superset of conventional logic – extended to handle partial truth. Soft-computing means computing not based on classical two-valued logics – includes fuzzy logic, neural networks, and probabilistic reasoning. 289 4/4/2011

Fuzzy Sets and Natural Language A discrimination function is a way to represent which objects are members of a set. –1 means an object is an element –0 means an object is not an element Sets using this type of representation are called “crisp sets” as opposed to “fuzzy sets”. Fuzzy logic plays the middle ground – like human reasoning – everything consists of degrees – beauty, height, grace, etc. 290 4/4/2011

Fuzzy Sets and Natural Language In fuzzy sets, an object may partially belong to a set measured by the membership function – grade of membership. A fuzzy truth value is called a fuzzy qualifier. Compatibility means how well one object conforms to some attribute. There are many type of membership functions. The crossover point is where  = 0.5 291 4/4/2011

Fuzzy Set Operations An ordinary crisp set is a special case of a fuzzy set with membership function [0, 1]. All definitions, proofs, and theorems of fuzzy sets must be compatible in the limit as the fuzziness goes to 0 and the fuzzy sets become crisp sets. 292 4/4/2011

Fuzzy Set Operations Set equalitySet Complement Set ContainmentProper Subset Set UnionSet Intersection Set ProductPower of a Set Probabilistic SumBounded Sum Bounded ProductBounded Difference ConcentrationDilation IntensificationNormalization 293

Fuzzy Relations A relation from a set A to a set B is a subset of the Cartesian product: A × B = {(a,b) | a  A and b  B} If X and Y are universal sets, then R = {  R (x, y) / (x, y) | (x, y)  X × Y} 294 4/4/2011

Fuzzy Relations The composition of relations is the net effect of applying one relation after another. For two binary relations P and Q, the composition of their relations is the binary relation: R(A, C) = Q(A, B)  P(B, C) 295 4/4/2011

Table 5.7 Some Applications of Fuzzy Theory 296 4/4/2011

Table 5.8 Some Fuzzy Terms of Natural Language 297 4/4/2011

Linguistic Variables One application of fuzzy sets is computational linguistics – calculating with natural language statements. Fuzzy sets and linguistic variables can be used to quantify the meaning of natural language, which can then be manipulated. Linguistic variables must have a valid syntax and semantics. 298 4/4/2011

Extension Principle The extension principle defines how to extend the domain of a given crisp function to include fuzzy sets. Using this principle, ordinary or crisp functions can be extended to work a fuzzy domain with fuzzy sets. This principle makes fuzzy sets applicable to all fields. 299 4/4/2011

Fuzzy Logic Just as classical logic forms the basis of expert systems, fuzzy logic forms the basis of fuzzy expert systems. Fuzzy logic is an extension of multivalued logic – the logic of approximate reasoning – inference of possibly imprecise conclusions from a set of possibly imprecise premises. 300 4/4/2011

Possibility and Probability and Fuzzy Logic In fuzzy logic, possibility refers to allowed values. Possibility distributions are not the same as probability distributions – frequency of expected occurrence of some random variable. 301 4/4/2011

Translation Rules Translation rules specify how modified or composite propositions are generated from their elementary propositions. 1. Type I modification rules 2. Type II composition rules 3. Type III quantification rules 4. Type IV quantification rules 302 4/4/2011

State of Uncertainty Commercial Applications There are two mountains – logic and uncertainty Expert systems are built on the mountain of logic and must reach valid conclusions given a set of premises – valid conclusions given that – –The rules were written correctly –The facts upon which the inference engine generates valid conclusions are true facts Today, fuzzy logic and Bayesian theory are most often used for uncertainty. 303 4/4/2011

Summary In this chapter, non-classical probability theories of uncertainty were discussed. Certainty factors, Dempster-Shafer and fuzzy theory are ways of dealing with uncertainty in expert systems. Certainty factors are simple to implement where inference chains are short (e.g. MYCIN) Certainty factors are not generally valid for longer inference chains. 304 4/4/2011

Summary Dempster-Shafer theory has a rigorous foundation and is used for expert systems. Fuzzy theory is the most general theory of uncertainty formulated to date and has wide applicability due to the extension principle. 305 4/4/2011

Chapter 6: Design of Expert Systems Expert Systems: Principles and Programming, Fourth Edition

307 Objectives Learn how to select an appropriate problem Learn the stages in the development of an expert system Anticipate certain types of errors in the development process Explore the role of the knowledge engineer in the building of an expert system Learn about life cycles and their models for expert systems

Expert Systems: Principles and Programming, Fourth Edition 308 Considerations when Building an Expert system We will describe the necessary prerequisites when building an expert system. The system should be a quality product. The development should be cost effective and timely. Designing of expert systems of part of a general field known as Knowledge Management (KM).

Expert Systems: Principles and Programming, Fourth Edition 309 Selecting the Appropriate Problem We need to answer the questions, “Why are we building this expert system?”. –Intellectual Property Agreement must be considered –Clearly identify the problem –Clearly identify the expert –Clearly identify the users We need to know the payoff – money, efficiency, etc.

Expert Systems: Principles and Programming, Fourth Edition 310 Selecting the Appropriate Problem What tools will be available to build the expert system? –Check the Web for applications in existence –Know the language necessary to create a semantic net of relationships on which the system will be based How much will the expert system cost? –A function of people, resources, time, etc. –How available is the knowledge?

Expert Systems: Principles and Programming, Fourth Edition 311 Figure 6.1 Project Management Tasks

Expert Systems: Principles and Programming, Fourth Edition 312 Stages in the Development How will the system be developed? –This will depend on the resources provided Stages: 1.Feasibility Study – see if the project is feasible 2.Rapid Prototype – demonstrate ideas / impress 3.Refined System – verification by knowledge engineers 4.Field Testable – system tested by selected users

Expert Systems: Principles and Programming, Fourth Edition 313 Stages in the Development 5.Commercial quality system – validation / testing 6.Maintenance and evolution – repair bugs, enhance capabilities

Expert Systems: Principles and Programming, Fourth Edition 314 Other Considerations How will the system be delivered? –Should be considered in earliest stages of development –Integration with existing programs How will the system be maintained and evolve? –Performance is dependent on knowledge / expertise –Performance must be maintained –New knowledge will be acquired –Old knowledge will be modified

Expert Systems: Principles and Programming, Fourth Edition 315 Figure 6.2 General Stages in the Development of an Expert System

Expert Systems: Principles and Programming, Fourth Edition 316 Errors in Development Stages Expert’s knowledge may be erroneous, propagating errors throughout the entire development process. –Formal procedures may be necessary to certify expert –Technique panels can scrutinize expert’s knowledge –Focus groups can also be used

Expert Systems: Principles and Programming, Fourth Edition 317 Errors in the Development Stages Knowledge may not be properly communicated to knowledge engineer, or knowledge may be misinterpreted. Knowledge base may be corrupted by entering incorrect form of a rule or fact.

Expert Systems: Principles and Programming, Fourth Edition 318 Errors in the Development Stages Inference engine errors may result from errors in pattern matching, conflict resolution, and execution of actions. Inference chain errors may be caused by erroneous knowledge, semantic errors, inference engine bugs, incorrect specifications of rule priorities, and strange interaction among rules. Limits of ignorance – a problem common to all previous stages

Expert Systems: Principles and Programming, Fourth Edition 319 Figure 6.3 Major Errors in Expert Systems and Some Causes

Expert Systems: Principles and Programming, Fourth Edition 320 Software Engineering and Expert Systems Expert systems are products like any other software product and require good standards for development. Expert systems may have serious responsibilities – life and death. High standards are a necessity and can be measured by “mean time between failures”.

Expert Systems: Principles and Programming, Fourth Edition 321 Figure 6.4 Software Engineering Methodology

Expert Systems: Principles and Programming, Fourth Edition 322 Expert System Life Cycle Begins with the initial concept of the software and ends with its retirement from use. Expert systems require more maintenance because they are based on knowledge that is: –Heuristic –Experiential A number of life cycle models have been developed.

Expert Systems: Principles and Programming, Fourth Edition 323 Waterfall Model Each stage ends with a verification and validation activity to minimize any problems in that stage. Arrows go back and forth only one stage at a time. It is assumed that all information necessary for a stage is known.

Expert Systems: Principles and Programming, Fourth Edition 324 Figure 6.5 Waterfall Model of the Software Life Cycle

Expert Systems: Principles and Programming, Fourth Edition 325 Code-and-Fix Model Some code is written and then fixed when it does not work correctly. Usually the method of choice for new programming students in conventional and expert systems This eventually led to the do-it-twice concept where a prototype then a final system was built.

Expert Systems: Principles and Programming, Fourth Edition 326 Incremental Model This is a refinement of the waterfall and top-down-approach. The idea is to develop software in increments of functional capability. –Major increment – assistant  colleague  expert –Minor increment – expertise w/in each level –Microincrement – add/refining individual rules

Expert Systems: Principles and Programming, Fourth Edition 327 Spiral Model Each circuit of the spiral adds some functional capability to the system.

Expert Systems: Principles and Programming, Fourth Edition 328 Detailed Life Cycle Model Linear Model 1.Planning Stage The purpose of this stage is to produce a formal work plan for the expert system development – documents to guide and evaluate the development.

Expert Systems: Principles and Programming, Fourth Edition 329 Table 6.2 Planning Stage Tasks

Expert Systems: Principles and Programming, Fourth Edition 330 Linear Model 2.Knowledge Definition The objective of this stage is to define the knowledge requirements of the expert system, which consists of two main tasks: Knowledge source identification and selection Knowledge acquisition, analysis, and extraction

Expert Systems: Principles and Programming, Fourth Edition 331 Table 6.3 Knowledge Source / Identification

Expert Systems: Principles and Programming, Fourth Edition 332 Table 6.4 Knowledge Acquisition, Analysis, and Extraction Tasks

Expert Systems: Principles and Programming, Fourth Edition 333 Linear Model 3.Knowledge Design The objective is to produce the detailed design for an expert system and involves: Knowledge definition Detailed design

Expert Systems: Principles and Programming, Fourth Edition 334 Table 6.5 Knowledge Definition Tasks

Expert Systems: Principles and Programming, Fourth Edition 335 Table 6.6 Detailed Design of Knowledge Tasks

Expert Systems: Principles and Programming, Fourth Edition 336 Linear Model 4.Code and Checkout This begins the actual code implementation

Expert Systems: Principles and Programming, Fourth Edition 337 Table 6.7 Code and Checkout Tasks

Expert Systems: Principles and Programming, Fourth Edition 338 Linear Model 5.Knowledge Verification The objective here is to determine the correctness, completeness, and consistency of the system. Formal tests Test Analysis

Expert Systems: Principles and Programming, Fourth Edition 339 Table 6.8 Formal Test Tasks of Knowledge Verification Stage

Expert Systems: Principles and Programming, Fourth Edition 340 Test Analysis Tasks

Expert Systems: Principles and Programming, Fourth Edition 341 Linear Model 6.System Evaluation This stage is for summarizing what has been learned with recommendations for improvements and corrections.

Expert Systems: Principles and Programming, Fourth Edition 342 Table 6.10 System Evaluation Stage Tasks

Expert Systems: Principles and Programming, Fourth Edition 343 Figure 6.7 Linear Model of Expert System Development Life Cycle

Expert Systems: Principles and Programming, Fourth Edition 344 Summary In this chapter, we have discussed a software engineering approach to the construction of an expert system. Principles about good interviewing techniques were mentioned. Now that expert systems are widely used, they must be quality products due to the sensitive nature of their applications.

Expert Systems: Principles and Programming, Fourth Edition 345 Summary Factors to be considered in the design of expert systems include problem selection, cost, and payoff. Both managerial and technical aspects must be considered. By consistently following the outlined “life cycle” it should be possible to construct quality software.

Chapter 7: Introduction to CLIPS Expert Systems: Principles and Programming, Fourth Edition

347 Objectives Learn what type of language CLIPS is Study the notation (syntax) used by CLIPS Learn the meaning of a field and what types exit Learn how to launch and exit from CLIPS Learn how to represent, add, remove, modified, and duplicated in CLIPS

Expert Systems: Principles and Programming, Fourth Edition 348 Objectives Learn how to debug programs using the watch command Learn how to use the deffacts construct to define a group of facts Learn how to use the agenda command and execute CLIPS programs Learn about commands that can manipulate constructs

Expert Systems: Principles and Programming, Fourth Edition 349 Objectives Learn how to use the printout command Learn how to use multiple rules Learn how to use the set-break command Learn how to use the load and save constructs Learn how to use variables, single and multifield wildcards, and comment constructs

Expert Systems: Principles and Programming, Fourth Edition 350 What is CLIPS? CLIPS is a multiparadigm programming language that provides support for: –Rule-based –Object-oriented –Procedural programming Syntactically, CLIPS resembles: –Eclipse –CLIPS/R2 –JESS

Expert Systems: Principles and Programming, Fourth Edition 351 Other CLIPS Characteristics CLIPS supports only forward-chaining rules. The OOP capabilities of CLIPS are referred to as CLIPS Object-Oriented Language (COOL). The procedural language capabilities of CLIPS are similar to languages such as: –C –Ada –Pascal –Lisp

Expert Systems: Principles and Programming, Fourth Edition 352 CLIPS Characteristics CLIPS is an acronym for C Language Integrated Production System. CLIPS was designed using the C language at the NASA/Johnson Space Center. CLIPS is portable – PC  CRAY.

Expert Systems: Principles and Programming, Fourth Edition 353 CLIPS Notation Symbols other than those delimited by, [ ], or { } should be typed exactly as shown. [ ] mean the contents are optional and mean that a replacement is to be made. * following a description means that the description can be replaced by zero or more occurrences of the specified value.

Expert Systems: Principles and Programming, Fourth Edition 354 CLIPS Notation Descriptions followed by + mean that one or more values specified by description should be used in place of the syntax description. A vertical bar | indicates a choice among one or more of the items separated by the bars.

Expert Systems: Principles and Programming, Fourth Edition 355 Fields To build a knowledge base, CLIPS must read input from keyboard / files to execute commands and load programs. During the execution process, CLIPS groups symbols together into tokens – groups of characters that have the same meaning. A field is a special type of token of which there are 8 types.

Expert Systems: Principles and Programming, Fourth Edition 356 Numeric Fields The floats and integers make up the numeric fields – simply numbers. Integers have only a sign and digits. Floats have a decimal and possibly “e” for scientific notation.

Expert Systems: Principles and Programming, Fourth Edition 357 Symbol Fields Symbols begin with printable ASCII characters followed by zero or more characters, followed by a delimiter. CLIPS is case sensitive.

Expert Systems: Principles and Programming, Fourth Edition 358 String Fields Strings must begin and end with double quotation marks. Spaces w/in the string are significant. The actual delimiter symbols can be included in a string by preceding the character with a backslash.

Expert Systems: Principles and Programming, Fourth Edition 359 Address Fields External addresses represent the address of an external data structure returned by a user-defined function. Fact address fields are used to refer to a specific fact. Instance Name / Address field – instances are similar to facts addresses but refer to the instance rather than a fact.

Expert Systems: Principles and Programming, Fourth Edition 360 Entering / Exiting CLIPS The CLIPS prompt is: CLIPS> This is the type-level mode where commands can be entered. To exit CLIPS, one types: CLIPS> (exit)  CLIPS will accept input from the user / evaluate it / return an appropriate response: CLIPS> (+ 3 4)   value 7 would be returned.

Expert Systems: Principles and Programming, Fourth Edition 361 Facts and CLIPS To solve a problem, CLIPS must have data or information with which to reason. Each chunk of information is called a fact. Facts consist of: –Relation name (symbolic field) –Zero or more slots w/associated values

Expert Systems: Principles and Programming, Fourth Edition 362 Example Fact in CLIPS

Expert Systems: Principles and Programming, Fourth Edition 363 Deftemplate Before facts can be constructed, CLIPS must be informed of the list of valid slots for a given relation name. A deftemplate is used to describe groups of facts sharing the same relation name and contain common information.

Expert Systems: Principles and Programming, Fourth Edition 364 Deftemplate General Format

Expert Systems: Principles and Programming, Fourth Edition 365 Deftemplate vs. Ordered Facts Facts with a relation name defined using deftemplate are called deftemplate facts. Facts with a relation name that does not have a corresponding deftemplate are called ordered facts – have a single implied multifield slot for storing all the values of the relation name.

Expert Systems: Principles and Programming, Fourth Edition 366 Adding Facts CLIPS store all facts known to it in a fact list. To add a fact to the list, we use the assert command.

Expert Systems: Principles and Programming, Fourth Edition 367 Displaying Facts CLIPS> (facts) 

Expert Systems: Principles and Programming, Fourth Edition 368 Removing Facts Just as facts can be added, they can also be removed. Removing facts results in gaps in the fact identifier list. To remove a fact: CLIPS> (retract 2) 

Expert Systems: Principles and Programming, Fourth Edition 369 Modifying Facts Slot values of deftemplate facts can be modified using the modify command:

Expert Systems: Principles and Programming, Fourth Edition 370 Results of Modification A new fact index is generated because when a fact is modified: –The original fact is retracted –The modified fact is asserted The duplicate command is similar to the modify command, except it does not retract the original fact.

Expert Systems: Principles and Programming, Fourth Edition 371 Watch Command The watch command is useful for debugging purposes. If facts are “watched”, CLIPS will automatically print a message indicating an update has been made to the fact list whenever either of the following has been made: –Assertion –Retraction

Expert Systems: Principles and Programming, Fourth Edition 372 Deffacts Construct The deffacts construct can be used to assert a group of facts. Groups of facts representing knowledge can be defined as follows: (deffacts [<optional] comment] * ) The reset command is used to assert the facts in a deffacts statement.

Expert Systems: Principles and Programming, Fourth Edition 373 The Components of a Rule To accomplish work, an expert system must have rules as well as facts. Rules can be typed into CLIPS (or loaded from a file). Consider the pseudocode for a possible rule: IF the emergency is a fire THEN the response is to activate the sprinkler system

Expert Systems: Principles and Programming, Fourth Edition 374 Rule Components First, we need to create the deftemplate for the types of facts: (deftemplate emergency (slot type)) -- type would be fire, flood, etc. Similarly, we must create the deftemplate for the types of responses: (deftemplate response (slot action)) -- action would be “activate the sprinkler”

Expert Systems: Principles and Programming, Fourth Edition 375 Rule Components The rule would be shown as follows: (defrule fire-emergency “An example rule” (emergency )type fire)) => (assert (response (action activate-sprinkler-system))))

Expert Systems: Principles and Programming, Fourth Edition 376 Analysis of the Rule The header of the rule consists of three parts: 1.Keyword defrule 2.Name of the rule – fire-emergency 3.Optional comment string – “An example rule” After the rule header are 1+ conditional elements – pattern CEs Each pattern consists of 1+ constraints intended to match the fields of the deftemplate fact

Expert Systems: Principles and Programming, Fourth Edition 377 Analysis of Rule If all the patterns of a rule match facts, the rule is activated and put on the agenda. The agenda is a collection of activated rules. The arrow => represents the beginning of the THEN part of the IF-THEN rule. The last part of the rule is the list of actions that will execute when the rule fires.

Expert Systems: Principles and Programming, Fourth Edition 378 The Agenda and Execution To run the CLIPS program, use the run command: CLIPS> (run [ ])  -- the optional argument is the maximum number of rules to be fired – if omitted, rules will fire until the agenda is empty.

Expert Systems: Principles and Programming, Fourth Edition 379 Execution When the program runs, the rule with the highest salience on the agenda is fired. Rules become activated whenever all the patterns of the rule are matched by facts. The reset command is the key method for starting or restarting. Facts asserted by a reset satisfy the patterns of one or more rules and place activation of these rules on the agenda.

Expert Systems: Principles and Programming, Fourth Edition 380 What is on the Agenda? To display the rules on the agenda, use the agenda command: CLIPS> (agenda)  Refraction is the property that rules will not fire more than once for a specific set of facts. The refresh command can be used to make a rule fire again by placing all activations that have already fired for a rule back on the agenda.

Expert Systems: Principles and Programming, Fourth Edition 381 Command for Manipulating Constructs The list-defrules command is used to display the current list of rules maintained by CLIPS. The list-deftemplates displays the current list of deftemplates. The list-deffacts command displays the current list of deffacts. The ppdefrule, ppdeftemplate and ppdeffacts commands display the text representations of a defrule, deftemplate, and a deffact, respectively.

Expert Systems: Principles and Programming, Fourth Edition 382 Commands The undefrule, undeftemplate, and undeffacts commands are used to delete a defrule, a deftemplate, and a deffact, respectively. The clear command clears the CLIPS environment and adds the initialfact-defacts to the CLIPS environment. The printout command can also be used to print information.

Expert Systems: Principles and Programming, Fourth Edition 383 Other Commands Set-break – allows execution to be halted before any rule from a specified group of rules is fired. Load – allows loading of rules from an external file. Save – opposite of load, allows saving of constructs to disk

Expert Systems: Principles and Programming, Fourth Edition 384 Commenting and Variables Comments – provide a good way to document programs to explain what constructs are doing. Variables – store values, syntax requires preceding with a question mark (?)

Expert Systems: Principles and Programming, Fourth Edition 385 Fact Addresses, Single-Field Wildcards, and Multifield Variables A variable can be bound to a fact address of a fact matching a particular pattern on the LHS of a rule by using the pattern binding operator “ <- ”. Single-field wildcards can be used in place of variables when the field to be matched against can be anything and its value is not needed later in the LHS or RHS of the rule. Multifield variables and wildcards allow matching against more than one field in a pattern.

Expert Systems: Principles and Programming, Fourth Edition 386 Summary In this chapter, we looked at the fundamental components of CLIPS. Facts make up the first component of CLIPS, made up of fields – symbol, string, integer, or float. The deftemplate construct was used to assign slot names to specific fields of a fact. The deffacts construct was used to specify facts as initial knowledge.

Expert Systems: Principles and Programming, Fourth Edition 387 Summary Rules make up the second component of a CLIPS system. Rules are divided into LHS – IF portion and the RHS – THEN portion. Rules can have multiple patterns and actions. The third component is the inference engine – rules having their patterns satisfied by facts produce an activation that is placed on the agenda.

Expert Systems: Principles and Programming, Fourth Edition 388 Summary Refraction prevents old facts from activating rules. Variables are used to receive information from facts and constrain slot values when pattern matching on the LHS of a rule. Variables can store fact addresses of patterns on the LHS of a rule so that the fact bound to the pattern can be retracted on the RHS of the rule. We also looked at single-field wildcards and multifield variables.

Chapter 8: Advanced Pattern Matching Expert Systems: Principles and Programming, Fourth Edition

390 Objectives Learn about field constraints Learn how to use functions and expressions Learn how to perform summing values using rules Learn how to use the bind function Learn how to use I/O functions

Expert Systems: Principles and Programming, Fourth Edition 391 Objectives Examine the two-player game called Sticks Learn how to use the test conditional element Learn how to use the predicate field constraint and the return value constraint Examine the or, and, not, exists, forall logical elements.

Expert Systems: Principles and Programming, Fourth Edition 392 Field Constraints In addition to pattern matching capabilities and variable bindings, CLIPS has more powerful pattern matching operators. Consider writing a rule for all people who do not have brown hair: –We could write a rule for every type of hair color that is not brown. –This involves testing the condition in a roundabout manner – tedious, but effective.

Expert Systems: Principles and Programming, Fourth Edition 393 Field Constraints The technique for writing a rule for all non- brown hair colors implies that we have the ability to supply all hair colors – virtually impossible. An alternative is to use a field constraint to restrict the values a field may have on the LHS – the THEN part of the rule.

Expert Systems: Principles and Programming, Fourth Edition 394 Connective Constraints Connective constraints are used to connect variables and other constraints. Not connective – the ~ acts on the one constraint or variable that immediately follows it. Or constraint – the symbol | is used to allow one or more possible values to match a field or a pattern. And constraint – the symbol & is useful with binding instances of variables and on conjunction with the not constraint.

Expert Systems: Principles and Programming, Fourth Edition 395 Combining Field Constraints Field constraints can be used together with variables and other literals to provide powerful pattern matching capabilities. Example #1: ?eyes1&blue|green –This constraint binds the person’s eye color to the variable, ?eyes1 if the eye color of the fact being matched is either blue or green. Example #2: ?hair1&~black –This constraint binds the variable ?hair1 if the hair color of the fact being matched is not black.

Expert Systems: Principles and Programming, Fourth Edition 396 Functions and Expressions CLIPS has the capability to perform calculations. The math functions in CLIPS are primarily used for modifying numbers that are used to make inferences by the application program.

Expert Systems: Principles and Programming, Fourth Edition 397 Numeric Expressions in CLIPS Numeric expressions are written in CLIPS in LISP-style – using prefix form – the operator symbol goes before the operands to which it pertains. Example #1: 5 + 8 (infix form)  + 5 8 (prefix form) Example #2: (infix) (y2 – y1) / (x2 – x1) > 0 (prefix) (> ( / ( - y2 y1 ) (- x2 x1 ) ) 0)

Expert Systems: Principles and Programming, Fourth Edition 398 Return Values Most functions (addition) have a return value that can be an integer, float, symbol, string, or multivalued value. Some functions (facts, agenda commands) have no return values – just side effects. Division results are usually rounded off. Return values for +, -, and * will be integer if all arguments are integer, but if at least one value is floating point, the value returned will be float.

Expert Systems: Principles and Programming, Fourth Edition 399 Variable Numbers of Arguments Many CLIPS functions accept variable numbers of arguments. Example: CLIPS> (- 3 5 7)  returns 3 - 5 =-2 - 7 = -9 There is no built-in arithmetic precedence in CLIPS – everything is evaluated from left to right. To compensate for this, precedence must be explicitly written.

Expert Systems: Principles and Programming, Fourth Edition 400 Embedding Expressions Expressions may be freely embedded within other expressions:

Expert Systems: Principles and Programming, Fourth Edition 401 Summing Values Using Rules Suppose you wanted to sum the areas of a group of rectangles. –The heights and widths of the rectangles can be specified using the deftemplate: (deftemplate rectangle (slot height) (slot width)) The sum of the rectangle areas could be specified using an ordered fact such as: (sum 20)

Expert Systems: Principles and Programming, Fourth Edition 402 Summing Values A deffacts containing sample information is: (deffacts initial-information (rectangle (height 10) (width 6)) (rectangle (height 7) (width 5) (rectangle (height 6) (width 8)) (rectangle (height 2) (width 5)) (sum 0))

Expert Systems: Principles and Programming, Fourth Edition 403 Summing Values

Expert Systems: Principles and Programming, Fourth Edition 404 The Bind Function Sometimes it is advantageous to store a value in a temporary variable to avoid recalculation. The bind function can be used to bind the value of a variable to the value of an expression using the following syntax: (bind )

Expert Systems: Principles and Programming, Fourth Edition 405 I/O Functions When a CLIPS program requires input from the user of a program, a read function can be used to provide input from the keyboard:

Expert Systems: Principles and Programming, Fourth Edition 406 Read Function from Keyboard The read function can only input a single field at a time. Characters entered after the first field up to the  are discarded. To input, say a first and last name, they must be delimited with quotes, “xxx xxx”. Data must be followed by a carriage return to be read.

Expert Systems: Principles and Programming, Fourth Edition 407 I/O from/to Files Input can also come from external files. Output can be directed to external files. Before a file can be accessed, it must be opened using the open function: Example: (open “mydata.dat” data “r”) mydata.dat – is the name of the file (path can also be provided)

Expert Systems: Principles and Programming, Fourth Edition 408 I/O from/to Files data – is the logical name that CLIPS associates with the file “r” – represents the mode – how the file will be used – here read access The open function acts as a predicate function –Returns true if the file was successfully opened –Returns false otherwise

Expert Systems: Principles and Programming, Fourth Edition 409 Table 8.2 File Access Modes

Expert Systems: Principles and Programming, Fourth Edition 410 Close Function Once access to the file is no longer needed, it should be closed. Failure to close a file may result in loss of information. General format of the close function: (close [ ]) (close data)  example

Expert Systems: Principles and Programming, Fourth Edition 411 Reading / Writing to a File Which logical name used, depends on where information will be written – logical name t refers to the terminal (standard output device).

Expert Systems: Principles and Programming, Fourth Edition 412 Formatting Output sent to a terminal or file may need to be formatted – enhanced in appearance. To do this, we use the format function which provides a variety of formatting styles. General format: (format *)

Expert Systems: Principles and Programming, Fourth Edition 413 Formatting Logical name – t – standard output device; or logical name associated with a file. Control string: –Must be delimited with quotes –Consists of format flags to indicate how parameters should be printed –1 – 1 correspondence between flags and number of parameters – constant values or expressions –Return value of the format function is the formatted string – nil can be used to suppress printing.

Expert Systems: Principles and Programming, Fourth Edition 414 Formatting Example: (format nil “Name: %-15s Age: %3d” “Bob Green” 35)  Produces the results: “Name: Bob green Age: 35”

Expert Systems: Principles and Programming, Fourth Edition 415 Specifications of Format Flag %-m.Nx The “-” means to left justify (right is the default) M – total field width – no truncation occurs N – number of digits of precision – default = 6 x – display format specification

Expert Systems: Principles and Programming, Fourth Edition 416 Table 8.3 Display Format Specifications

Expert Systems: Principles and Programming, Fourth Edition 417 Readline Function To read an entire line of input, the readline function can be used: (readline [ ]) Example: (defrule get-name => (printout t “What is your name? “ (bind ?response (readline)) (assert (user’s-name ?response)))

Expert Systems: Principles and Programming, Fourth Edition 418 Predicate Functions A predicate function is defined to be any function that returns: –TRUE –FALSE Any value other than FALSE is considered TRUE. We say the predicate function returns a Boolean value.

Expert Systems: Principles and Programming, Fourth Edition 419 The Test Conditional Element Processing of information often requires a loop. Sometimes a loop needs to terminate automatically as the result of an arbitrary expression. The test condition provides a powerful way to evaluate expressions on the LHS of a rule. Rather than pattern matching against a fact in a fact list, the test CE evaluates an expression – outermost function must be a predicate function.

Expert Systems: Principles and Programming, Fourth Edition 420 Test Condition A rule will be triggered only if all its test CEs are satisfied along with other patterns. (test ) Example: (test (> ?value 1))

Expert Systems: Principles and Programming, Fourth Edition 421 Predicate Field Constraint The predicate field constraint : allows for performing predicate tests directly within patterns. The predicate field constraint is more efficient than using the test CE. It can be used just like a literal field constraint – by itself or part of a complex field. The predicate field constraint is always followed by a function for evaluation (predicate function).

Expert Systems: Principles and Programming, Fourth Edition 422 Return Value Constraint The return value constraint = allows the return value of a function to be used for comparison inside a pattern. The return value constraint must be followed by a function (not necessarily a predicate function). The function must have a single-field return value.

Expert Systems: Principles and Programming, Fourth Edition 423 The OR Conditional Element Consider the two rules:

Expert Systems: Principles and Programming, Fourth Edition 424 OR Conditional Element These two rules can be combined into one rule – or CE requires only one CE be satisfied:

Expert Systems: Principles and Programming, Fourth Edition 425 The And Conditional Element The and CE is opposite in concept to the or CE – requiring all the CEs be satisfied:

Expert Systems: Principles and Programming, Fourth Edition 426 Not Conditional Element When it is advantageous to activate a rule based on the absence of a particular fact in the list, CLIPS allows the specification of the absence of the fact in the LHS of a rule using the not conditional element:

Expert Systems: Principles and Programming, Fourth Edition 427 Not Conditional We can implement this as follows:

Expert Systems: Principles and Programming, Fourth Edition 428 The Exists Conditional Element The exists CE allows one to pattern match based on the existence of at least one fact that matches a pattern without regard to the total number of facts that actually match the pattern. This allows a single partial match or activation for a rule to be generated based on the existence of one fact out of a class of facts.

Expert Systems: Principles and Programming, Fourth Edition 429 Exists Conditional

Expert Systems: Principles and Programming, Fourth Edition 430 Exists When more than one emergency fact is asserted, the message to the operators is printed more than once. The following modification prevents this:

Expert Systems: Principles and Programming, Fourth Edition 431 Exists This assumes there was already an alert – triggered by an operator-emergency rule. What if the operators were merely placed on alert to a drill:

Expert Systems: Principles and Programming, Fourth Edition 432 Exists Now consider how the rule has been modified using the exists CE: (defrule operator-alert-for-emergency (exists (emergency)) (not (operator-alert)) => (printout t “Emergency: Operator Alert” crlf) (assert (operator-alert))))

Expert Systems: Principles and Programming, Fourth Edition 433 Forall / Logical Conditional Elements The forall CE allows one to pattern match based on a set of CEs that are satisfied for every occurrence of another CE. The logical CE allows one to specify that the existence of a fact depends on the existence of another fact or group of facts.

Expert Systems: Principles and Programming, Fourth Edition 434 Summary We have introduced the concept of field constraints – not, and, and or. Functions are entered into CLIPS top-level command loop or are used on the LHS or RHS of a rule. –Many functions have variable number of arguments –Function calls can be nested w/in other function calls

Expert Systems: Principles and Programming, Fourth Edition 435 Summary CLIPS provides several I/O functions. –Open / close –Printout / read / readline –Format Various concepts for controlling the flow of execution are available Also included in the discussion were the following CEs: –Test, And, Or, Exists, Forall, and logical

Chapter 9: Modular Design, Execution Control, and Rule Efficiency Expert Systems: Principles and Programming, Fourth Edition

437 Objectives Learn how to use deftemplate attributes See how salience and modules can be used to control the execution of rules Determine the different phases for fault detection, isolation, and recover Explore some misuses of salience Learn how to use the defmodule construct

Expert Systems: Principles and Programming, Fourth Edition 438 Objectives Learn how to import / export facts Learn how modules affect execution control Analyze the Rete pattern-matching algorithm Learn the role of the pattern network, join network, pattern order, and how to order patterns for efficiency Learn the role of multifield variables and efficiency

Expert Systems: Principles and Programming, Fourth Edition 439 Objectives Learn the role of the test CE and efficiency Determine the difference between general and specific rules Determine the difference between simple and complex rules

Expert Systems: Principles and Programming, Fourth Edition 440 Deftemplate Attributes CLIPS provides slot attributes which can be specified when deftemplate slots are defined. Slot attributes provide strong typing and constraint checking. One can define the allowed types that can be stored in a slot, range of numeric values. Multislots can specify min / max numbers of fields they can contain. Default attributes can be provided for slots not specified in an assert command.

Expert Systems: Principles and Programming, Fourth Edition 441 Type Attribute Defines the data types can be placed in a slot Example: (deftemplate person (multislot name (type SYMBOL)) (SLOT AGE (TYPE integer))) Once defined, CLIPS will enforce these restrictions on the slot attributes name – must store symbols age – must store integers

Expert Systems: Principles and Programming, Fourth Edition 442 Static and Dynamic Constraint Checking CLIPS provides two levels of constraint checking –Static constraint checking Performed when CLIPS parses expression / constant Can be disabled by calling the set-static-constraint-checking function and passing it FALSE –Dynamic constraint checking Performed on facts when they are asserted Can be enabled / disabled with set-dynamic-constraint- checking

Expert Systems: Principles and Programming, Fourth Edition 443 Allowed Value Attributes CLIPS allows one to specify a list of allowed values for a specific type – 8 are provided: SymbolsStrings LexemesIntegers FloatsNumbers Instance-namesValues

Expert Systems: Principles and Programming, Fourth Edition 444 Range Attributes This attribute allows the specification of minimum and maximum numeric values. Example: (deftemplate person (multislot name (type SYMBOL)) (slot age (type INTEGER) (range 0 ?VARIABLE)))

Expert Systems: Principles and Programming, Fourth Edition 445 Default Attribute Previously, each deftemplate fact asserted had an explicit value stated for every slot. It is often convenient to automatically have a specified value stored in a slot if no value is explicitly stated in an assert command. –Example: (default )  can be either ?DERIVE or ?NONE or single expression, zero or more expressions

Expert Systems: Principles and Programming, Fourth Edition 446 Default-Dynamic Attribute When the default attribute is used, the default value for a slot is determined when the slot definition is parsed or when the fact that will use the default value is asserted.

Expert Systems: Principles and Programming, Fourth Edition 447 Conflicting Slot Attributes CLIPS does not allow you to specify conflicting attributes for a slot.

Expert Systems: Principles and Programming, Fourth Edition 448 Salience CLIPS provides two explicit techniques for controlling the execution of rules: –Salience –Modules Salience allows the priority of rules to be explicitly specified. The agenda acts like a stack (LIFO) – most recent activation placed on the agenda being first to fire.

Expert Systems: Principles and Programming, Fourth Edition 449 Salience Salience allows more important rules to stay at the top of the agenda, regardless of when they were added. Lower salience rules are pushed lower on the agenda; higher salience rules are higher. Salience is set using numeric values in the range - 10,000  +10,000 – zero is intermediate priority. Salience can be used to force rules to fire in a sequential fashion.

Expert Systems: Principles and Programming, Fourth Edition 450 Salience Rules of equal salience, activated by different patterns are prioritized based on the stack order of facts. If 2+ rules with same salience are activated by the same fact, no guarantee about the order in which they will be place on the agenda.

Expert Systems: Principles and Programming, Fourth Edition 451 Phases and Control Facts

Expert Systems: Principles and Programming, Fourth Edition 452 Figure 9.2 Assignment of Salience for Different Phases

Expert Systems: Principles and Programming, Fourth Edition 453 Implementation of System Approaches: 1.Embed the control knowledge directly into the rules. Example: Detection rules would include rules indicating when the isolation phase should be entered. Each group of rules would be given a pattern indicating in which phase it would be applicable.

Expert Systems: Principles and Programming, Fourth Edition 454 Implementation 2.Use salience to organize the rules. 3.Separate the control knowledge from the domain knowledge. Each rule is given a control pattern that indicates its applicable phase. Control rules are then written to transfer control between the different phases.

Expert Systems: Principles and Programming, Fourth Edition 455 Salience Hierarchy Salience hierarchy is a description of the salience values used by an expert system. Each level corresponds to a specific set of rules whose members are all given the same salience. When rules for detection / isolation / recovery are zero, salience hierarchy is:

Expert Systems: Principles and Programming, Fourth Edition 456 Misuse of Salience Because salience is such a powerful tool, allowing explicit control over execution, there is potential for misuse. Well-designed rule-based programs should allow inference engine to control firings in an optimal manner. Salience should be used to determine the order when rules fire, not for selecting a single rule from a group of rules when patterns can control criteria for selection.

Expert Systems: Principles and Programming, Fourth Edition 457 Rule of Thumb No more than seven salience values should ever be required for coding an expert system – bested limited to 3 – 4. For large expert systems, programmers should use modules to control the flow of execution – limited to 2 – 3 salience values.

Expert Systems: Principles and Programming, Fourth Edition 458 The Defmodule Construct Up to now, all defrules, deftemplates, and deffacts have been contained in a single work space. CLIPS uses the defmodule construct to partition a knowledge base by defining the various modules. Syntax: (defmodule [ ])

Expert Systems: Principles and Programming, Fourth Edition 459 The MAIN Module By default, CLIPS defines a MAIN module as seen below: CLIPS>(clear)  CLIPS>(DEFTEMPLATE SENSOR (SLOT NAME))  CLIPS>(PPDEFTEMPLATE sensor)  CLIPS>(DEFTEMPLATE MAIN::sensor (slot name))  CLIPS> The symbol :: is called the module separator

Expert Systems: Principles and Programming, Fourth Edition 460 Examples of Defining Modules Examples: CLIPS> (defmodule DETECTION)  CLIPS> (defmodule ISOLATION)  CLIPS> (defmodule RECOVERY)  By default, CLIPS puts newly defined constructs in the current module – MAIN, when CLIPS starts or is cleared. When a new modules is defined, it becomes current.

Expert Systems: Principles and Programming, Fourth Edition 461 Examples To override this, the module where the construct will be placed can be specified in the construct’s name: CLIPS> (defule ISOLATION::example2 =>)  CLIPS> (ppdefrule example2)  (defrule ISOLATION::example2 =>) CLIP>

Expert Systems: Principles and Programming, Fourth Edition 462 Examples To find out which module is current: CLIPS> (get-current-module)  To change the current module: CLIPS> (set-current-module DETECTION)  Specifying modules in commands: CLIPS> (list-defrules RECOVERY) 

Expert Systems: Principles and Programming, Fourth Edition 463 Specifying Modules in Commands By default, most CLIPS commands operating on a construct work only on the constructs contained in the current module. CLIPS> (list-defrules RECOVERY)  Note the list-defrules command accepts a module name as an optional argument.

Expert Systems: Principles and Programming, Fourth Edition 464 Importing and Exporting Facts Just as constructs can be partitioned by placing them in separate modules, facts can also be partitioned. Asserted facts are automatically associated with the module in which their corresponding deftemplates are defined. The facts command can accept a module name as an argument: (facts [ ] [ [ [ ]]])

Expert Systems: Principles and Programming, Fourth Edition 465 Importing/Exporting Facts Unlike defrule and deffacts constructs, deftemplate constructs can be shared with other modules. A fact is “owned” by the module in which its deftemplate is contained. The owning module can export the deftemplate associated with the fact making that fact and all other facts using that deftemplate visible to other modules.

Expert Systems: Principles and Programming, Fourth Edition 466 Importing/Exporting Facts It is not sufficient just to export the deftemplate to make a fact visible to another module. To use a deftemplate defined in another module, a module must also import the deftemplate definition. A construct must be defined before it can be specified in an import list, but it does not have to be defined before it can be specified in an export list; so, it is impossible for two modules to import from each other.

Expert Systems: Principles and Programming, Fourth Edition 467 Modules and Execution Control The defmodule construct can be used to control the execution of rules. Each module defined in CLIPS has its own agenda. Execution can be controlled by deciding which module’s agenda is selected for executing rules.

Expert Systems: Principles and Programming, Fourth Edition 468 The Agenda Command To display activations for the current module: CLIPS> (agenda)  To display activations for the DETECTION module: CLIPS> (agenda DETECTION) 

Expert Systems: Principles and Programming, Fourth Edition 469 The Focus Command Assume there are rules on several agendas – when the run command is issued, no rules fire. CLIPS maintains a current focus that determines which agenda the run command uses during execution. The reset and clear commands automatically set the current focus to the MAIN module. The current focus does not change when the current module is changed.

Expert Systems: Principles and Programming, Fourth Edition 470 The Focus Command To change the current focus: CLIPS> (focus +)  Example: CLIPS> (focus DETECTION)  TRUE CLIPS> (run)  Now rules on the DETECTION module will be fired.

Expert Systems: Principles and Programming, Fourth Edition 471 The Focus Command The focus command also recalls the previous value of the current focus – the top of the stack data structure called the focus stack. When the focus command changes the current focus, it is pushing a new current focus onto the top of the stack. As rules execute, the current focus becomes empty, is popped from the focus stack, the next module becomes the current focus until empty.

Expert Systems: Principles and Programming, Fourth Edition 472 Manipulating/Examining the Focus Stack CLIPS provides several commands for manipulating the current focus and stack: 1.Clear-focus-stack – removes all modules from focus stack 2.Get-focus – returns module name of current focus or FALSE if empty 3.Pop-focus – removes current focus from stack or FALSE if empty

Expert Systems: Principles and Programming, Fourth Edition 473 Manipulating/Examining the Focus Stack 4.Get-focus-stack – returns a multifield value containing the modules on the focus stack 5.Watch command – can be used to see changes in the focus stack 6.Return command – terminate execution of a rule’s RHS, remove current focus from focus stack, return execution to next module 7.Auto-focus – changes default that a rule’s module is not auto focused upon when that rule is activated.

Expert Systems: Principles and Programming, Fourth Edition 474 The Rete Pattern-Matching Algorithm Rule-based languages like CLIPS use the Rete Pattern-Matching Algorithm for matching facts against the patterns in rules to determine which rules have had their conditions satisfied. If the matching process occurs only once, the inference engine simply examines each rule and then searches the set of facts to see if the rule’s patterns have been satisfied – if so it is place on the agenda.

Expert Systems: Principles and Programming, Fourth Edition 475 Rete Algorithm In rule-based languages, the matching process takes place repeatedly and the fact list is modified on each cycle of the execution. Such changes can cause previously unsatisfied patterns to be satisfied or vice versa – as facts are added/removed, the set of rules must be updated/maintained. Checking rules against facts each cycle is a slow process.

Expert Systems: Principles and Programming, Fourth Edition 476 Rete Algorithm Unnecessary computation can be avoided by remembering what has already been matched from cycle to cycle and computing only necessary changes. The Rete algorithm uses temporal redundancy to save the state of the matching process from cycle to cycle, recomputing changes in this state only for the change that occurred in the fact list.

Expert Systems: Principles and Programming, Fourth Edition 477 Rete Algorithm The Rete algorithm also takes advantage of structural similarity in a rules.

Expert Systems: Principles and Programming, Fourth Edition 478 Pattern Network Problems related to matching facts can be divided into two steps: 1.When facts are added and removed it must be determined which patterns have been matched. 2.Comparison of variable bindings across patterns must be checked to determine the partial matches for a group of patterns. This process is performed in the pattern network – similar to a tree.

Expert Systems: Principles and Programming, Fourth Edition 479 Join Network Once it has been determined which patterns have been matched by facts, comparison of variable binding across patterns must be checked to ensure that variables used in more than one pattern have consistent values. This is done in the join network which takes advantage of structural similarity by sharing joins between rules.

Expert Systems: Principles and Programming, Fourth Edition 480 Importance of Pattern Order Because the Rete algorithm saves the state from one cycle to the next, it is important to make sure that rules do not generate large numbers of partial matches.

Expert Systems: Principles and Programming, Fourth Edition 481 Guidelines for Ordering Patterns Guidelines: –Place the most specific pattern toward the front of the LHS of a rule. –Patterns matching against facts frequently added/removed from fact list should be placed toward the end of the LHS of a rule. –Patterns that will match very few facts in the fact list should be placed near the front of the rule.

Expert Systems: Principles and Programming, Fourth Edition 482 General Rules vs. Specific Rules 1.Specific rules tend to isolate much of the pattern-matching process in the pattern network, reducing the amount of work in the join network. 2.General rules often provide more opportunity to sharing in the pattern and join networks. 3.A single rule can also be more maintained than a group. 4.General rules need to be written carefully.

Expert Systems: Principles and Programming, Fourth Edition 483 Simple Rules vs. Complex Rules 1.The easiest way to code a problem in a rule- based language is not necessarily the best. 2.The number of comparisons performed can often be reduced by using temporary facts to store data.

Expert Systems: Principles and Programming, Fourth Edition 484 Summary We discussed various CLIPS features to aid in developing more robust expert systems. Deftemplate attributes permit enforcement of type and value constraints. Salience provides a mechanism for more complex control structures to prioritize rules. The defmodule construct allows a knowledge base to be partitioned. We demonstrated the importance of matching facts against rules efficiently.

Expert Systems: Principles and Programming, Fourth Edition 485 Summary Rules are converted into data structures in a rule network consisting of a pattern network and a join network. Ordering of patterns can have a significant effect on the performance of rules. The most specific patterns and patterns matching the fewest facts should be placed first in the LHS of a rule, volatile patterns last in the LHS of a rule.

Chapter 10: Procedural Programming Expert Systems: Principles and Programming, Fourth Edition

487 Objectives Learn how to use procedural functions to control the flow of execution Learn how to use the deffunction construct to define new functions Learn how to use the defglobal construct to define global variables Learn how to use the defgeneric and defmethod constructs to define generic functions and their methods

Expert Systems: Principles and Programming, Fourth Edition 488 Objective Learn how defglobal, deffunction, and defgeneric constructs can be imported and exported by modules Learn how to use utility commands and functions

Expert Systems: Principles and Programming, Fourth Edition 489 Procedural Function CLIPS provides several functions for controlling the flow of execution: if function while function switch functionloop-for-count function progn$ functionbreak function halt function

Expert Systems: Principles and Programming, Fourth Edition 490 if Function General format: (if then + [else +]) The predicate expression is first evaluated. If the condition is anything other than FALSE, the THEN clause actions are executed; otherwise, the ELSE actions are executed (unless omitted).

Expert Systems: Principles and Programming, Fourth Edition 491 while Function General format: (while [do] *) The predicate expression is evaluated before actions of the body are executed. If the evaluation is anything other than FALSE, expressions in the body are executed; if FALSE, control passes to the next statement after the while.

Expert Systems: Principles and Programming, Fourth Edition 492 switch Function General format: (switch * [ ]) Test-expression is evaluated first. Each comparison expression is then evaluated in order defined and if it matches the value of comparison expression, actions after then are executed, then termination. If no match, default statement actions are executed (if present).

Expert Systems: Principles and Programming, Fourth Edition 493 loop-for-count Function General format: (loop-for-count [do] <expression*) The body of the function is executed a given number of times, depending on. This is similar to the “for” loop structure in most high-level languages.

Expert Systems: Principles and Programming, Fourth Edition 494 progn$ Function General format: (progn$ (expression>*) can be : - body is executed once for each field - field of current iteration is retrieved by referencing variable. Special variable is created by appending –index to - index of current iteration

Expert Systems: Principles and Programming, Fourth Edition 495 break Function General format: (break) The break function terminates the execution of the while, loop-for-count, or progn$ function in which it is immediately contained. It causes early termination of a loop when a specified condition has been met.

Expert Systems: Principles and Programming, Fourth Edition 496 halt Function General format: (assert (phrase halt)) The halt function can be used on the RHS of a rule to stop execution of rules on the agenda. When called, no further actions will be executed from the RHS of the rule being fired and control returns to the top-level prompt.

Expert Systems: Principles and Programming, Fourth Edition 497 The Deffunction Construct CLIPS allows one to define functions similar to the way it is done in procedural languages. New functions are defined using the deffunction construct: (deffunction [ ] ( * [ ]) *)  the body of the deffunction

Expert Systems: Principles and Programming, Fourth Edition 498 Deffunction The body of the deffunction is a series of expressions similar to the RHS of a rule that are executed in order when the deffunction is called. Deffunctions can be deleted and the watch command can be used to trace their execution.

Expert Systems: Principles and Programming, Fourth Edition 499 Deffunction The and declarations allow you to specify the arguments that will be passed to the deffunction when it is called. A deffunction can return values – value returned is the value of the last expression evaluated within the body of the deffunction.

Expert Systems: Principles and Programming, Fourth Edition 500 return Function General format: (return [ ]) In addition to terminating the execution of the RHS of a rule, the return function also allows the currently executing deffunction to be terminated.

Expert Systems: Principles and Programming, Fourth Edition 501 Recursions and Forward Declarations Deffunctions can call other functions within their body as well as themselves – a recursive call. Sometimes functions make circular references to one another. To accomplish this: –A forward declaration is done –The name and argument list of the function are declared –The function body is left empty –The forward declaration can be replaced later with a version that includes the body

Expert Systems: Principles and Programming, Fourth Edition 502 Watching Deffunctions When deffunctions are watched using the watch command, an informational message is printed whenever a deffunction begins or ends execution. The >> symbol means that a deffunction is being entered, and the << symbol means it is being exited.

Expert Systems: Principles and Programming, Fourth Edition 503 Wildcard Parameters If all arguments in the parameter list of a deffunction are single-field variables: –1-1 correspondence between these parameters and the number of arguments that must be passed when the deffunction is called. If the last parameter declared in a deffunction is a multifield variable (wildcard parameter) –Deffunction can be called with more arguments than are specified in the parameter list.

Expert Systems: Principles and Programming, Fourth Edition 504 Deffunction Commands 1.ppdeffunction – displays the text representation of a deffunction 2.pndeffunction – used to delete a deffunction 3.list-deffunction – displays the list of deffunctions defined. 4.get-deffunction-list – returns a multifield value containing the list of deffunctions

Expert Systems: Principles and Programming, Fourth Edition 505 User-Defined Function User-defined functions are functions written in the C language and called from CLIPS.

Expert Systems: Principles and Programming, Fourth Edition 506 The Defglobal Construct CLIPS allows one to define global variables that retain their values outside the scope of a construct. Local variables are local to the construct that references them.

Expert Systems: Principles and Programming, Fourth Edition 507 Defining Global Variables The defglobal construct has the following format: (defglobal [ ] *) Defmodule-name = module in which globals will be defined (current if omitted) Global-assignment = = Expression = provides the initial value for the global variable Global-variable = ?*

Expert Systems: Principles and Programming, Fourth Edition 508 Global Variables By entering the global variable’s name at the command line, one can see its value. References to such variables can be made anywhere it is legal to use an. They cannot be used as a parameter of a deffunction. Can only be used on the LHS of a rule if contained within a function call.

Expert Systems: Principles and Programming, Fourth Edition 509 Global Commands 1.ppdefglobal – displays text representation of a defglobal 2.undefglobal – deletes a defglobal 3.list-defglobals – displays list of defglobals defined in CLIPS 4.show-defglobals – displays names and values of defglobals defined in CLIPS 5.get-defglobal-list – returns multifield value containing the list of defglobals.

Expert Systems: Principles and Programming, Fourth Edition 510 The Defgeneric and Defmethod Constructs Generic functions is a group of related functions sharing a common name. Overloaded functions are those w/more than one method. When called, the method w/signature matching is the one executed – process called generic dispatch

Expert Systems: Principles and Programming, Fourth Edition 511 Defgeneric / Defmethod General formats: (defgeneric [ ]) (defmethod [index>] [ ] [ * [ ]) *)

Expert Systems: Principles and Programming, Fourth Edition 512 Defmethods Specific defmethods do not have names but are assigned a unique integer index that can be used to reference the method. You can assign a specific index or CLIPS will assign one for you. For a given generic function, each method must have a unique signature from the other methods in that generic function.

Expert Systems: Principles and Programming, Fourth Edition 513 Regular Parameter Restrictions Each can be one of two forms: –A single-field variable (as in a deffunction), or –Of the form ( * [ ])

Expert Systems: Principles and Programming, Fourth Edition 514 Method Precedence When a generic function is called, CLIPS executes only one of the methods. Generic dispatch is the process of determining which method will execute. CLIPS defines a precedence order to the methods defined for a particular method. The preview-generic command displays the precedence order.

Expert Systems: Principles and Programming, Fourth Edition 515 Precedence Given two methods of a generic function, CLIPS uses the following steps to determine which method has the higher precedence – see Fig. 10.1. 1.Compare the leftmost unexamined parameter restrictions of the two methods. If only one method has parameters left, proceed to step 6. If neither method has parameters remaining, proceed to step 7. Otherwise, proceed to step 2.

Expert Systems: Principles and Programming, Fourth Edition 516 Precedence 2.If one parameter is a regular parameter and the other parameter is a wildcard parameter, then the method with the regular parameter has precedence. Otherwise, proceed to step 3. 3.If one parameter has type restrictions and the other parameter does not, then the method with the types restrictions has precedence. Otherwise, proceed to step 4. 4.Compare the leftmost unexamined type restriction of the two parameters. If the type restrictions on one parameter is more specific than on the other, the method with more restrictions has precedence.

Expert Systems: Principles and Programming, Fourth Edition 517 Precedence 5.If one parameter has a query restriction and the other does not, then the method with the query restriction has precedence. Otherwise return to step 1 and compare the next set of parameters. 6.If the next parameter of the method with parameters remaining is a regular parameter, then this method has precedence. Otherwise, the other method has precedence. 7.The method that was defined first has precedence.

Expert Systems: Principles and Programming, Fourth Edition 518 Query Restrictions A query restriction is a defglobal reference or function that is evaluated to determine the applicability of a method when a generic function is called. If the query restriction evaluates to false, the method is not applicable. The query restriction for a parameter is not evaluated unless the type restrictions for that parameter are satisfied. A method w/multiple parameters can have multiple query restrictions and each must be satisfied for the method to be applicable.

Expert Systems: Principles and Programming, Fourth Edition 519 Defmethod Commands 1.ppdefmethod – displays the text representation of a defmethod 2.undefmethod – deletes a defmethod 3.list-defmethods – displays the list of defmethods defined in CLIPS 4.get-defmethod-list – returns a multifield value containing the list of defmethods

Expert Systems: Principles and Programming, Fourth Edition 520 Defgeneric Commands 1.ppdefgeneric – displays the text representation of a defgeneric 2.undefgeneric – deletes a defgeneric 3.list-defgenerics – displays the list of defgenerics defined in CLIPS 4.get-defgeneric-list – returns a multifield value containing the list of defgenerics

Expert Systems: Principles and Programming, Fourth Edition 521 Procedural Constructs and Defmodules Similar to deftemplate constructs, defglobal, and defgeneric constructs can be imported and exported by modules. Four of the export/import statements previously discussed apply to procedural constructs: 1.(export ?ALL) 2.(export ?NONE) 3.(import ?ALL) 4.(import ?NONE)

Expert Systems: Principles and Programming, Fourth Edition 522 Procedural Constructs and Defmodules 1.This format will export all exportable constructs from a module. 2.Indicates that no constructs are exported. 3.Imports all exported constructs from the specified module. 4.Imports none of the exported constructs from the specified module.

Expert Systems: Principles and Programming, Fourth Edition 523 Useful Commands / Functions 1.load-facts / save-facts – allows facts to be loaded from / saved to a file. 2.system command – allows execution of O/S commands within CLIPS – not all O/S provides functionality for this command. 3.batch command – allows commands and responses normally entered at the top-level to be read from a file. 4.dribble-on / dribble-off – session capture

Expert Systems: Principles and Programming, Fourth Edition 524 Commands / Functions 5.random – generates random integer values 6.string-to-field – converts a string-field value into a field. 7.apropos command – displays al symbols defined in CLIPS containing a specific substring 8.sort function – sorts a list of fields

Expert Systems: Principles and Programming, Fourth Edition 525 Summary The if, while, switch, loop-for-count, progn$, and break functions can alter the flow in a deffunction, generic function, or on the RHS of a rule – overuse is considered poor programming. The deffunction construct allows for defining new functions (similar to procedural languages) The defglobal construct allows one to define global variables that retain values outside the scope of constructs.

Expert Systems: Principles and Programming, Fourth Edition 526 Summary Generic functions (w/defgeneric / defmethod) provide more power and flexibility than deffunctions by associating multiple procedural methods with a generic function name. When generic functions are called, generic dispatch examines the types of arguments passed to the function and evaluates any associated query restrictions to determine the appropriate method to invoke.

Expert Systems: Principles and Programming, Fourth Edition 527 Summary Several utility commands were introduced, including: save-factsload-facts systembatch dribble-on/offrandom sortapropos string-to-field

Chapter 11: Classes, Instances, and Message- Handlers Expert Systems: Principles and Programming, Fourth Edition

529 Objectives Learn how to define a class using the defclass construct See how to create instances of classes using the make-instance command Use message-handlers to attach procedural information to a class Use the definstances construct to create a set of specific instances of a class Learn about inheritance between classes

Expert Systems: Principles and Programming, Fourth Edition 530 Objectives See how pattern matching works with objects Create user-defined message-handlers Learn how to control slot access Survey the different types of message-handlers Create instances, initialization, and deletion of message-handlers

Expert Systems: Principles and Programming, Fourth Edition 531 Objectives Learn how to modify and duplicate instances Learn how to uses classes and generic functions Learn how to use instance set query functions Learn about multiple inheritance between classes See how to import and export defclass constructs and how to load and save instances

Expert Systems: Principles and Programming, Fourth Edition 532 The Defclass Construct Before instances of classes can be created, CLIPS needs to know the list of valid slots for the given class. To provide this information, the defclass construct is used: (defclass [ ] (is-a ) *)

Expert Systems: Principles and Programming, Fourth Edition 533 The Defclass Construct Note that is the class from which the newly defined class will inherit information. All user-defined classes ultimately inherit from the system class USER. A user-defined class will therefore inherit from the USER class or from another user-defined class.

Expert Systems: Principles and Programming, Fourth Edition 534 The Slot Definition The syntax of the is: (slot * | (multislot *) type, range, cardinality, allowed-symbols allowed-strings, allowed-lexemes, allowed-integers allowed-floats, allowed-numbers, allowed-values allowed-instance-names, default, and default-dynamic

Expert Systems: Principles and Programming, Fourth Edition 535 Creating Instances To create an instance of a class, use the make- instance command as follows: (make-instance [ ] of *) where is: ( ) Instances belong to the module in which their corresponding defclass is defined.

Expert Systems: Principles and Programming, Fourth Edition 536 System-Defined Message- Handlers Just like data, procedural information can be attached to classes. Such procedures are called message-handlers. –User-defined –System-defined – automatically created Message handlers can be invoked for an instance (object) using the send command. (send *)

Expert Systems: Principles and Programming, Fourth Edition 537 System Message-Handlers For each slot defined in a defclass, CLIPS automatically defines get- and put-slot message- handlers that are used to retrieve and set slot values. The get-message-handlers have no arguments and return the value of the slot. The put-message-handlers take zero or more arguments. If not arguments are supplied, the slot is restored to its original default-value.

Expert Systems: Principles and Programming, Fourth Edition 538 System Message-Handlers Supplying the arguments will set the slot value to those values. The return value of a put-message-handler is the new value of the slot. When slots are being watched, an informational message is printed whenever the value of an instance slot is changed. When instances are watched, an informative message appears when an instance is created/deleted.

Expert Systems: Principles and Programming, Fourth Edition 539 The Definstances Construct The definstances construct is the equivalent of the deffacts construct. When a reset command is issued, all instances are sent a delete message. Then all instances found in the definstances constructs are created.

Expert Systems: Principles and Programming, Fourth Edition 540 Definstances Construct General format: (definstances [active] [ ] *) Where is: ([instance-name-expression>] of *)

Expert Systems: Principles and Programming, Fourth Edition 541 Definstances Construct By default, pattern matching does not occur for definstances instances until all the slot overrides have been processed. Several commands exist for manipulating definstances: list-definstances – displays list of definstances maintained by CLIPS ppdefinstances – displays text representations of definstances undefinstances – deletes definstances get-definstances-list – returns multifield value containing list of definstances

Expert Systems: Principles and Programming, Fourth Edition 542 Classes and Inheritance One benefit of using COOL is class inheritance. Classes allow us to share common information among multiple classes w/o duplication of information or inclusion of unnecessary information. It is possible for a class to redefine a slot that was already defined by one of its superclasses.

Expert Systems: Principles and Programming, Fourth Edition 543 Classes and Inheritance Definitions: –Subclass – class that inherits directly/indirectly from another class –Superclass – class from which subclass inherits A single-inheritance class hierarchy is one in which each class has only one direct superclass. A multiple-inheritance hierarchy (COOL) is where a class may have more than one direct superclass.

Expert Systems: Principles and Programming, Fourth Edition 544 Classes and Inheritance The source slot attribute allows slot attributes to inherit from superclasses. The default value for the slot is exclusive – attributes determined by most specific class defining the slot. –Single inheritance – the class having fewest superclasses Source slot composite, attributes not explicitly defined in most specific class defining slot are taken from next most specific that defines the attribute.

Expert Systems: Principles and Programming, Fourth Edition 545 Classes and Inheritance It is possible to disable the inheritance of a slot using the propagation slot attribute: –Inherit (default) – slot will be inherited by subclasses –No-inherit – slot will not be inherited by subclasses It is possible to define classes to be used only for inheritance – abstract classes; instances cannot be created – concrete by default. The role class attribute specifies whether a class is abstract or concrete.

Expert Systems: Principles and Programming, Fourth Edition 546 Commands to Manipulate Defclasses 1.list-defclasses – displays the current list of defclasses maintained by CLIPS 2.browse-classes – displays the inheritance relationships between a class and its subclasses 3.ppdefclass – displays the text representation of a defclass 4.undefclass – deletes a defclass

Expert Systems: Principles and Programming, Fourth Edition 547 Object Pattern Matching Single object pattern can match instances from several classes. Changes to slot values that are not specified in an object pattern do not retrigger the rule to which the pattern belongs. Changes to slot values that are not specified in an object pattern within a logical conditional element do not remove logical support provided by the associated rule.

Expert Systems: Principles and Programming, Fourth Edition 548 Object Pattern General format: (object *) where is: (is-a ) | (name ) | ( *) and is the same as the pattern slot constraints that are used in deftemplate patterns.

Expert Systems: Principles and Programming, Fourth Edition 549 Object Patterns One difference between object patterns and fact patterns is that only those object patterns that explicitly match on a slot are affected when the slot value of an instance is changed. It is possible to force a slot or a class not to participate in pattern matching using the pattern- match attribute. Object patterns and instance creation can be used in conjunction with the logical conditional element just as facts and facts patterns can.

Expert Systems: Principles and Programming, Fourth Edition 550 Object Patterns Changes in instance slots do not affect the logical support for a fact or instance if the slot was not referenced in an object pattern within the logical conditional element. When using object patterns in a rule, CLIPS will sometimes use the initial-object/fact pattern. If so, an initial-fact pattern is added if the pattern preceding the insertion position is a fact pattern – if an object pattern an initial-object pattern is inserted.

Expert Systems: Principles and Programming, Fourth Edition 551 User-Defined Message-Handlers In addition to print, delete, put, and get-system- defined message handlers, COOL automatically defines for each class, you can define your own message-handlers using defmessage-handler. General format: (defmessage-handler [ ] ( * [ ]) *)

Expert Systems: Principles and Programming, Fourth Edition 552 User-Defined Message-Handlers By default, a message-handler is a primary message-handler. Each class has its own set of message-handlers. The body of the message-handler, represented by *, behaves like the body of a deffunction. The bind function can be used to bind local variables and the last expression evaluated in the body is the value returned.

Expert Systems: Principles and Programming, Fourth Edition 553 Slot Shorthand References A shorthand mechanism allows one to access the slots of the instances bound to the ?self variable. The expression: ?self: can be used to retrieve the value of a slot. Similarly, (bind ?self:slot-name *) can be used to set a slot value. Both bypass message-passing and directly manipulate slots and can only be used for slots directly defined by the class.

Expert Systems: Principles and Programming, Fourth Edition 554 Encapsulation COOL supports object encapsulation –Hiding the details of the class –Limiting access to the class via a well-defined interface – message-handlers defined for the class If the visibility slot attribute is set to private (default), the slot can only be directly accessed by message-handlers of class defining it; if public, slot can be directly accessed by subclasses and superclasses defining it.

Expert Systems: Principles and Programming, Fourth Edition 555 Watching Messages & Message Handlers If message-handlers or messages are watched with watch, informational messages are printed when a message-handler begins or ends execution. Defmessage-handler commands –list-defmessage-handlers – displays current list of defmessage-handlers maintained by CLIPS –ppdefmessage-handler – displays text representation of defmessage-handler –undefmessage-handler – deletes a defmessage-handler –get-defmessage-handler – returns multifield value containing list of defmessage-handlers for class

Expert Systems: Principles and Programming, Fourth Edition 556 Slot Access and Handler Creation The access and create-accessor slot attributes control the access of slots. Access attribute restricts the type of access allowed to slots – read-write, read-only, initialize only. The create-accessor attribute controls the automatic creation of the get- and put-handlers for class slots – read-write, read-only, write-only, and none.

Expert Systems: Principles and Programming, Fourth Edition 557 Before/After/Around Message-Handlers When an existing class does not meet your needs and may depend on other code to maintain its behavior – unfamiliar code or code you don’t want to modify. To get around this, you can define a new class that will inherit whatever behavior you want from the existing class. Message-handler can be one of four types: primary, before/after, and around.

Expert Systems: Principles and Programming, Fourth Edition 558 Before/After/Around Message-Handlers 1.Primary handler – (default) typically the main handler for responding to a message, overrides / shadows primary message-handler for same message inherited from a superclass. 2.Before/After handlers – invoked before and after the primary handler, respectively. 3.Around handlers – must explicitly invoke the other handler types.

Expert Systems: Principles and Programming, Fourth Edition 559 Before/After/Around Message-Handlers 4.#2 and #3 handlers of superclasses are now shadowed by subclass definition. 5.It is possible to override the arguments passed to a message-handler by using the override- next-handler command.

Expert Systems: Principles and Programming, Fourth Edition 560 Handler Execution Order With all the available techniques to modify class behaviors, which is the best approach? A class can slightly modify the behavior of a superclass using before and after handler w/o overriding the primary handler. Subclass cannot prevent the execution of a before or after handler unless they terminate the message – preventing the execution of all before, after, and primary handlers.

Expert Systems: Principles and Programming, Fourth Edition 561 Handler Execution Order If an existing class’s behavior is modified by redefining a new class and then overriding the primary handler, the primary handler is also subject to being overridden by a subclass. Unless the overriding class calls the call-next- handler function, the primary handler will not be executed. See page 670 of the text for steps.

Expert Systems: Principles and Programming, Fourth Edition 562 Instance Creation, Initialization, and Deletion Message-Handlers 1.create -- is called after an instance is created but before any default values or slot overrides have been applied. 2.init – is called after slot overrides have been processed to set any remaining slot values that were not overridden to their default values 3.delete – either explicitly called to delete an instance or automatically called when you call make-instance and specify instance name of existing instance.

Expert Systems: Principles and Programming, Fourth Edition 563 Modifying / Duplicating Instances 1.modify-instance – slot values are changed directly by the direct-modify message-handler w/o invoking message passing. 2.message-modify-instance – same as #1 but uses message-passing to change slot values. 3.active-modify 4.active-message-modify

Expert Systems: Principles and Programming, Fourth Edition 564 Commands for Duplicating Instances 1.duplicate-instance 2.message-duplicate-instance 3.active-duplicate-instance 4.active-message-modify-instance

Expert Systems: Principles and Programming, Fourth Edition 565 Instance Set Query Functions 1.Any-instancep function – if a set of instances is found that satisfy the query, then the any-instancep function returns the symbol TRUE; otherwise FALSE. 2.Find-instance query function – returns a multifield value containing the first instance set satisfying the query, then the multifield value will be empty. 3.find-all-instances function – returns a multifield value containing all instance sets satisfying the query. 4.Do-for-instance, do-for-all-instances, and delayed-do- for-all allow actions on the instance sets satisfying a query.

Expert Systems: Principles and Programming, Fourth Edition 566 Multiple Inheritance Specifying: –Single inheritance – a single class specified in the is-a attribute –Multiple inheritance – specify more than one class in the is-a attribute

Expert Systems: Principles and Programming, Fourth Edition 567 Multiple Inheritance Conflicts The most practical examples involve cases where the superclass from which the class is inheriting do not share slots or message-handlers – no conflicts occur here. In simple cases where the classes specified in the is-a attribute do not share common user-defined superclasses, the order in which the classes are specified determines the precedence when there are multiple definitions of the same slot or message-handler.

Expert Systems: Principles and Programming, Fourth Edition 568 Defclasses and Defmodules In a similar manner to other constructs, defclass constructs can be imported and exported by modules. The export and import statements previously discussed which export or import all constructs, also apply to defclasses. Explicit specifying which defclasses are exported or imported is possible.

Expert Systems: Principles and Programming, Fourth Edition 569 Loading and Saving Instances 1.save-instances command – saves instances to a file 2.load-instances command – loads in a group of instances stored in a file 3.bsave- and boad-instances command – similar to #1 and #2, except binary format is used

Expert Systems: Principles and Programming, Fourth Edition 570 Summary This chapter introduced the CLIPS Object- Oriented Language (COOL) Instances (objects) are another data representation provided by CLIPS. Instance attributes are specified using the defclass construct. Procedural code is implemented using the defmessage-handler construct. Inheritance allows classes to make use of slots and message-handlers associated with another class.

Expert Systems: Principles and Programming, Fourth Edition 571 Summary COOL supports single- and multiple- inheritance. In addition to the slot attributes provided with deftemplates, several additional slot attributes are also supported by defclasses. Several predefined system message-handlers for creating, initializing, printing, and deleting instances are available. User-defined message-handlers can also be created.

Expert Systems: Principles and Programming, Fourth Edition 572 Summary Message-handlers are invoked by sending an instance a message name along with associated arguments via the send command Object pattern matching provides several capabilities not found with fact pattern matching. Finally, COOL provides several instance set query functions that allow direct queries on sets of instances satisfying a specified set of conditions.

Chapter 12: Expert Systems Design Examples Expert Systems: Principles and Programming, Fourth Edition

574 Summary Learn about incorporating uncertainty into CLIPS The use of decision trees to provide a useful paradigm Learn how to emulate backward chaining in a CLIPS program Constructing a framework of simpler expert systems into one large program

Expert Systems: Principles and Programming, Fourth Edition 575 Uncertainty Factors Although CLIPS has no built-in capabilities for handling uncertainty, it is possible to incorporate uncertainty into CLIPS by placing information dealing with uncertainty directly into the facts and rules. We begin by exploring how uncertainty was incorporated into the expert system MYCIN.

Expert Systems: Principles and Programming, Fourth Edition 576 MYCIN and Uncertainty 1.MYCIN represents factual information as object-attribute-value (OAV) triplets. 2.MYCIN also associates with each fact a certainty factor (CF) which represents a degree of belief in the fact. -1 means the fact is false 0 means no information is known about the fact 1 means the fact is known to be true

Expert Systems: Principles and Programming, Fourth Edition 577 MYCIN and Uncertainty 3.Because CLIPS does not handle uncertainty factors automatically, a slot in each fact will be used to represent the uncertainty factor. 4.MYCIN allows the same OAV triple to be derived by separate rules. The OAV triples are then combined producing a single OAV triple that combines the certainty factors.

Expert Systems: Principles and Programming, Fourth Edition 578 MYCIN and Uncertainty 5.In order to allow identical OAV triples to be asserted with the same certainty factors, the set- fact-duplication command can be used to disable the CLIPS behavior preventing duplicated facts from being asserted. 6.MYCIN combines two identical OAV triples into a single OAV triple with a combined uncertainty, computed as: New Uncertainty = (CF 1 + CF 2 ) – (CF 1 * CF 2 )

Expert Systems: Principles and Programming, Fourth Edition 579 MYCIN and Uncertainty 7.Since CLIPS does not automatically handle certainty factors for facts, it does not automatically combine two OAV triplets derived from different rules. The combination is handled by a rule that searches the fact list for identical OAV triples to be combined. 8. Next, it is necessary to link the certainty factors of the facts that match the LHS of the rule to the certainty factors of the facts asserted by the RHS of the rule – CF of LHS < 0.2 will not fire.

Expert Systems: Principles and Programming, Fourth Edition 580 MYCIN and Uncertainty 9.The certainty factor of a fact asserted from the RHS of a rule is derived by multiplying the CF of the assertion by the certainty factor of the LHS of the rule.

Expert Systems: Principles and Programming, Fourth Edition 581 MYCIN and Uncertainty 10.The single combine-certainties method handles only the case where both certainty factors are positive. By additional methods the other cases of certainty can be handled.

Expert Systems: Principles and Programming, Fourth Edition 582 Decision Trees 1.Decision trees provide a useful paradigm for solving certain types of classification problems. 2.Decision trees derive solutions by reducing the set of possible solutions with a series of decisions or questions that prune their search space. 3.Problems suitable for decision trees are those that provide the answer to a problem from a predetermined set of possible answers.

Expert Systems: Principles and Programming, Fourth Edition 583 Decision Trees 4.Decision trees consist of nodes and branches, connecting parent nodes to child from the top to bottom. The top node (root) has no parent. Every other node has only one parent. Nodes with no children are leaves. 5.Leaf nodes represent all possible solutions that can be derived from the tree – answer nodes. All other nodes are decision nodes.

Expert Systems: Principles and Programming, Fourth Edition 584 Decision Trees 6.In general, a decision node may use any criteria to select which branch to follow as long as it yields only one branch. The branch selected may be a set or range of values, etc. 7.The procedure to traverse a tree to reach an answer is simple – begin at root, if the current node is decision, answer the question – YES, move left, NO, move right. When answer node is current location value is derived from the decision tree.

Expert Systems: Principles and Programming, Fourth Edition 585 Decision Trees with Multiple Branches A binary decision tree may prove inefficient – not allowing for a set of responses or a series of cases. A modified decision tree allows for multiple branches – giving a series of possible decisions.

Expert Systems: Principles and Programming, Fourth Edition 586 Figure 12.1 Binary Decision Tree

Expert Systems: Principles and Programming, Fourth Edition 587 Figure 12.2 Decision Tree with Multiple Branches

Expert Systems: Principles and Programming, Fourth Edition 588 Decision Trees That Learn Sometimes it is useful to add new knowledge to a decision tree. Learning can result in the decision tree becoming unbalanced – efficient decision trees are balanced.

Expert Systems: Principles and Programming, Fourth Edition 589 Figure 12.3 Animal Identification Decision Tree

Expert Systems: Principles and Programming, Fourth Edition 590 Figure 12.4 Animal Identification Decision Tree After Learning Bird

Expert Systems: Principles and Programming, Fourth Edition 591 A Rule-Based Decision Tree Program The first step implementing the learning process in a decision tree in CLIPS is to decide how knowledge should be represented. Since the tree should learn, the tree should be represented as facts instead of rules – facts are easily added / deleted from a tree. A set of CLIPS rules can be used to traverse the decision tree by implementing the Solve_Tree_and_Learn algorithm using rule- based approach.

Expert Systems: Principles and Programming, Fourth Edition 592 A Rule-Based Decision Tree Program Each node of the tree should be represented by a fact. From one run to the next, information about what has been learned will be stored in a file. The rules for traversal of the tree must be determined.

Expert Systems: Principles and Programming, Fourth Edition 593 Backward Chaining CLIPS does not directly implement backward chaining but it can be emulated using forward chaining CLIPS rules. Backward chaining rules are represented as facts and acted on by the CLIPS backward-chaining inference rules.

Expert Systems: Principles and Programming, Fourth Edition 594 Backward Chaining 1.Backward chaining rules are represented as facts so the antecedents and consequents can be examined by rules that will act as a backward chaining inference engine. 2.As backward chaining proceeds, subgoals will be generated to determine the value of attributes. A fact will be needed to represent information about goal attributes. Ordered facts will be used to represent goal attributes.

Expert Systems: Principles and Programming, Fourth Edition 595 Backward Chaining 3.The backward chaining inference engine can be implemented with two sets of rules. 1.The first group generates goals for attributes and asks the user to supply attribute values when these values cannot be determined by rules. 2.The second group of rules will perform update operations, including modifying rules when their conditions have been satisfied and removing goals when they have been satisfied.

Expert Systems: Principles and Programming, Fourth Edition 596 A Monitoring Program Problem Statement: The problem to be solved is an example of a simple monitoring system – well suited for forward chaining rule-based languages. –Input consists of sensor values read during program cycles. –Inference occurs until all possible conclusions can be derived from the input data are reached.

Expert Systems: Principles and Programming, Fourth Edition 597 A Monitoring Program The Details Needed to Begin: Several problem specifications are necessary before an expert system for any example can be built. –First, the expected behavior of the expert system should be specified. –Decisions should be made regarding whether or not to broaden specifications to allow future modifications or upgrading. –It must be determined how data is to be retrieved.

Expert Systems: Principles and Programming, Fourth Edition 598 A Monitoring Program Knowledge Definitions: Determine how the knowledge should be represented.

Expert Systems: Principles and Programming, Fourth Edition 599 A Monitoring Program Control of Execution: Determine the phases of the monitoring process – –Read values from sensors, associate guard line and red line conditions for sensor values and determine developing trends. –After trends have been established, the system should issue warnings, shut down, restart, etc.

Expert Systems: Principles and Programming, Fourth Edition 600 A Monitoring Program Reading the Raw Sensor Values: Sensor values will be read from a file.

Expert Systems: Principles and Programming, Fourth Edition 601 A Monitoring Program Detecting a Trend: Determine the current state of the sensors and calculate trends that may be developing.

Expert Systems: Principles and Programming, Fourth Edition 602 A Monitoring Program Issue Warnings: The final phase is the warning phase. –Sensors having entered red line regions will have their associated devices shut off. –Sensors staying w/in guard line region for a number of cycles will have their devices shut off. –Sensors in guard line region and did not have their devices shut off will have a warning issued

Expert Systems: Principles and Programming, Fourth Edition 603 Summary This chapter demonstrated a technique for representing MYCIN-style certainty factors in CLIPS. Facts are used to represent OAV triplets. An additional slot in each fact represents the certainty factor of the fact. Rules are used to compute certainty values for newly asserted facts on the on RHS of a rule using certainty factors bound in the LHS of rule.

Expert Systems: Principles and Programming, Fourth Edition 604 Summary Decision trees can be represented using the forward chaining paradigm of CLIPS. There are several algorithms for traversing decision trees. The algorithm for a multiple-branch decision tree that learns is implemented in CLIPS. CLIPS can emulate backward chaining inference strategy; backward chaining rules are represented as facts and acted on by backward chaining inference rules.

Expert Systems: Principles and Programming, Fourth Edition 605 Summary The final example was a simple monitoring expert system.

Chapter 1: Introduction to Expert Systems Expert Systems: Principles and Programming, Fourth Edition.

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Chapter 1: Introduction to Expert Systems Expert Systems: Principles and Programming, Fourth Edition.

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