© C. Kemke1Reasoning - Introduction COMP 4200: Expert Systems Dr. Christel Kemke Department of Computer Science University of Manitoba

© C. Kemke2Reasoning - Introduction Reasoning in Expert Systems  knowledge representation in Expert Systems  shallow and deep reasoning  forward and backward reasoning  alternative inference methods  metaknowledge

© C. Kemke3Reasoning - Introduction Expert performance depends on expert knowledge! Experts and Expert Systems  Human Experts achieve high performance because of extensive knowledge concerning their field  Generally developed over many years

© C. Kemke4Reasoning - Introduction Types of Knowledge Knowledge Representation in XPS can include:  conceptual knowledge  terminology, domain-specific terms  derivative knowledge  conclusions between facts  causal connections  causal model of domain  procedural knowledge  guidelines for actions

© C. Kemke5Reasoning - Introduction Knowledge Modeling in XPS Knowledge Modeling Technique in XPS  mostly rule-based systems (RBS)  rule system models elements of knowledge formulated independently as rules  rule set is easy to expand  often only limited ‘deep’ knowledge, i.e. no explicit coherent causal or functional model of the domain

© C. Kemke6Reasoning - Introduction Shallow and Deep Reasoning  shallow reasoning  also called “experiential reasoning”  aims at describing aspects of the world heuristically  short inference chains  complex rules  deep reasoning  also called causal reasoning  aims at building a model that behaves like the “real thing”  long inference chains  simple rules that describe cause and effect relationships

© C. Kemke7Reasoning - Introduction Dilbert on Reasoning 1

© C. Kemke8Reasoning - Introduction Dilbert on Reasoning 2

© C. Kemke9Reasoning - Introduction Dilbert on Reasoning 3

© C. Kemke10Reasoning - Introduction General Technology of XPS Knowledge + Inference  core of XPS  Most often Rule-Based Systems (RBS)  other forms: Neural Networks, Case-Based Reasoning

© C. Kemke11Reasoning - Introduction Rule-Based Expert Systems Work with  a set of facts describing the current world state  a set of rules describing the expert knowledge  inference mechanisms for combining facts and rules in reasoning

© C. Kemke12Reasoning - Introduction Inference Engine Agenda Knowledge Base (rules) Explanation Facility User Interface Knowledge Acquisition Facility Working Memory (facts)

© C. Kemke13Reasoning - Introduction Architecture of Rule-Based XPS 1 Knowledge-Base / Rule-Base  stores expert knowledge as “condition-action-rules” (or: if- then- or premise-consequence-rules)  objects or frame structures are often used to represent concepts in the domain of expertise, e.g. “club” in the golf domain. Working Memory  stores initial facts and generated facts derived by the inference engine  additional parameters like the “degree of trust” in the truth of a fact or a rule (  certainty factors) or probabilistic measurements can be added

© C. Kemke14Reasoning - Introduction Architecture of Rule-Based XPS 2 Inference Engine  matches condition-part of rules against facts stored in Working Memory (pattern matching);  rules with satisfied condition are active rules and are placed on the agenda;  among the active rules on the agenda, one is selected (see conflict resolution, priorities of rules) as next rule for  execution (“firing”) – consequence of rule can add new facts to Working Memory, modify facts, retract facts, and more

© C. Kemke15Reasoning - Introduction Architecture of Rule-Based XPS 3 Inference Engine + additional components might be necessary for other functions, like  calculation of certainty values,  determination of priorities of rules  and conflict resolution mechanisms,  a truth maintenance system (TMS) if reasoning with defaults and beliefs is requested

© C. Kemke16Reasoning - Introduction Rule-Based Systems - Example ‘Grades’ - Rules to determine ‘grade’ 1. study  good_grade 2. not_study  bad_grade 3. sun_shines  go_out 4. go_out  not_study 5. stay_home  study 6. awful_weather  stay_home

© C. Kemke17Reasoning - Introduction Example ‘Grades’ 1. study  good_grade 2. not_study  bad_grade 3. sun_shines  go_out 4. go_out  not_study 5. stay_home  study 6. awful_weather  stay_home Q1: If the weather is awful, do you get a good or bad grade? Q2: When do you get a good grade? Rule-Base to determine the ‘grade’:

© C. Kemke18Reasoning - Introduction Forward and Backward Reasoning forward reasoning  Facts are given. What is the conclusion? A set of known facts is given (in WM); apply rules to derive new facts as conclusions (forward chaining of rules) until you come up with a requested final goal fact. backward reasoning  Hypothesis (goal) is given. Is it supported by facts? A hypothesis (goal fact) is given; try to derive it based on a set of given initial facts using sub-goals (backward chaining of rules) until goal is grounded in initial facts.

© C. Kemke19Reasoning - Introduction 1.study  good_grade 2.not_study  bad_grade 3.sun_shines  go_out 4.go_out  not_study 5.stay_home  study 6.awful_weather  stay_home Example ‘Grades’ forward reasoningrule chain given fact: awful_weather 6,5,1 backward reasoning hypothesis/goal: good_grade 1,5,6

© C. Kemke20Reasoning - Introduction good grade Example ‘Grades’ – Reasoning Tree bad grade not studystudy go outstay home sun shinesawful weather

© C. Kemke21Reasoning - Introduction Example – Grades Working MemoryAgenda awful weatherRule 6 Select and apply Rule 6 awful weather stay home Rule 5 Select and apply Rule 5

© C. Kemke22Reasoning - Introduction Example – Grades Working MemoryAgenda Select and apply Rule 1 awful weather stay home study Rule 1 awful weather stay home study good grade empty DONE!

© C. Kemke23Reasoning - Introduction forward reasoning: Shield AND Pistol  Police backward reasoning: Police  Badge AND gun Police BadgeGun Shield PistolRevolver AND OR Bad Boy Example ‘Police’ – Reasoning Tree Q: What if only ‘Gun’ is known?

© C. Kemke24Reasoning - Introduction Police BadgeGun Shield PistolRevolver AND OR Bad Boy Example ‘Police’ – Reasoning Tree Q: What if only ‘Pistol’ is known as ground fact?

© C. Kemke25Reasoning - Introduction Police BadgeGun Shield PistolRevolver AND OR Bad Boy Example ‘Police’ – Reasoning Tree Task: Write down the Rule-Base for this example!

© C. Kemke26Reasoning - Introduction Forward vs. Backward Chaining Forward ChainingBackward Chaining diagnosisconstruction data-drivengoal-driven (hypothesis) bottom-up reasoningtop-down reasoning find possible conclusions supported by given facts find facts that support a given hypothesis antecedents (LHS) control evaluation consequents (RHS) control evaluation

© C. Kemke27Reasoning - Introduction Alternative Reasoning Methods  Theorem Proving  emphasis on mathematical proofs and correctness, not so much on performance and ease of use  Probabilistic Reasoning  integrates probabilities into the reasoning process  Certainty Factors  Express subjective assessment of truth of fact or rule  Fuzzy Reasoning  allows the use of vaguely defined predicates and rules

© C. Kemke28Reasoning - Introduction Metaknowledge  deals with “knowledge about knowledge”  e.g. reasoning about properties of knowledge representation schemes, or inference mechanisms  usually relies on higher order logic  in (first order) predicate logic, quantifiers are applied to variables  second-order predicate logic allows the use of quantifiers for function and predicate symbols  may result in substantial performance problems  CLIPS uses meta-knowledge to define itself, i.e. CLIPS constructs, classes, etc. - in a bootstrapping form