2. The Representation of Knowledge 1 Session 3
Course : T0273 – EXPERT SYSTEMS
Year : 2014
The Representation of Knowledge 1 Session 3

3. Learning Outcomes LO 1 : Explain concepts of Expert Systems
LO 2 : Describe the characteristics of Expert Systems
After taking this course, students should be able to explain and discuss the representation of knowledge.
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4. Lecture Outline Introduction The Meaning of Knowledge Productions
Semantic Nets
Object-Attribute-Value Triples
PROLOG and Semantic Nets
Difficulties with Semantic Nets
Schemata
Graph
Exercise
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5. Introduction
Knowledge representation (KR) has long been considered central to AI because it is as significant a factor in determining the success of a system as the software that uses the knowledge.
KR is of major importance in expert systems for two reasons:
Expert systems are designed for a certain type of KR based on rules of logic called inferences.
Expert systems affects the development, efficiency, speed, and maintenance of the system.
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6. The Meaning of Knowledge
Knowledge is one of those words that everyone knows the meaning of, yet finds hard to define or even feel the same way about.
Knowledge has many meanings depending on the mind of the beholder
Knowledge can be further classified into procedural knowledge, declarative knowledge, and tacit knowledge.
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7. The Meaning of Knowledge
Procedural Knowledge
is often referred to as knowing how to do something.
Example: knowing how to boil a pot of water
Declarative Knowledge
refers to knowing that something is true or false
Example: “Don’t put your fingers in a pot of boiling water”
Tacit Knowledge (unconscious knowledge)
it cannot be expressed by language.
Example: knowing how to move your hand.
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8. The Meaning of Knowledge
Knowledge is of primary importance in expert systems.
Knowledge + Inference = Expert Systems
Inferencing is generally used for mechanical systems such as expert systems.
Reasoning is the term used for human thinking.
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9. The Meaning of Knowledge
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10. Productions
A number of different KR techniques have been devised. These include rules, semantic nets, frames, script, logic, conceptual graphs, and others.
One formal notation for defining productions is the Backus-Naur-Form (BNF).
Example:
<sentence>::= <subject> <verb> <end-mark>
angle brackets, <>, and ::= : symbols of the metalanguage
term within angle brackets : nonterminal symbols
<sentence> : start symbol
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11. Productions The production rule:
<sentence>  <subject> <verb> <end-mark>
states that a sentence is composed of a subject, followed by a verb, followed by an end-mark
The following rules complete the nonterminals by specifying their possible terminals. The bar means “or” in the metalanguage:
<subject>  I | You | We
<verb>  left |came
<end-mark>  . | ? | !
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12. Productions
A grammar is a complete set of production rules that defines a language ambiguously.
Example:
<sentence>  <subject> <verb> <object> <end-mark>
<object>  home | work | school
A parse tree or derivation tree is a graphic representation of a sentence decomposed into all the terminals and nonterminals used to derive the sentence
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13. Semantics Nets
Semantic network is a classic AI representation technique used for propositional information. Sometimes called a propositional net.
The structure of a semantic net is shown graphically in terms of nodes and the arcs connecting them. Nodes are sometimes referred as objects and the arcs as links or edges.
The links of a semantic net are used to express relationships. Nodes are generally used to represent physical objects, concepts, or situations.
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14. Semantics Nets
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15. A Semantic Net with IS-A and A-Kind-Of (AKO) Links
Semantics Nets
A Semantic Net with IS-A and A-Kind-Of (AKO) Links
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16. Object-Attribute-Value Triples
An object-attribute-value triple (OAV), or triplet, can be used to characterize all the knowledge in a semantic net and was used in the expert system MYCIN for diagnosing infectious diseases.
OAV triples are especially useful for representing facts and the patterns to match the facts in the antecedent of a rule.
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17. Object-Attribute-Value Triples
An OAV Table
Object
Attribute
Value
apple
grapes
color
type
quantity
red
macintosh
100
seedless
500
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18. PROLOG and Semantic Nets
Semantic nets are easy to translate into PROLOG.
Example:
is_a (goodyear_blimp, blimp).
is_a (spirit_of_st_louis, special).
has_shape (blimp, ellipsoidal).
has_shape (balloon, round).
Each of statements above is a PROLOG predicate expression.
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19. PROLOG and Semantic Nets
The following are some examples of PROLOG predicates. Comments are preceded by semicolons and ignored by the PROLOG engines:
color (red). ; red is a color is a fact
mother (pat, ann). ; pat is the mother of ann
parents (jim, ann, tom) ; jim and ann are parents of tom
surrogatemother (pat, tom) ; pat is surrogatemother of tom
Predicates can also be expressed with relations such as the IS-A and HAS-A
is_a (red, color).
has_a (john, father).
has_a (john, mother).
has_a (john, parents).
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20. Difficulties with Semantic Nets
The lack of link name standards.
Combinatorial explosion of searching nodes, especially if the response to a query is negative.
It would take a very long time to answer negative queries.
Semantic nets are logically inadequate because they cannot define knowledge in the way that logic can.
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21. Schemata
In AI, the term schema (plural schemas or schemata) is used to describe a more complex knowledge structure than the semantic net.
For example, the acts of eating and drinking are pleasurable sensorimotor schemata that involve coordinating information from the senses with the required motor (muscle) movements to eat and drink.
A conceptual schema is an abstraction in which specific objects are classified by their general properties. For example, if you see a small red, round object with a green stem under a sign that says Artificial Fruit, you will recognize it as an artificial apple.
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22. Graph
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.
Bina Nusantara University

23. 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.
Bina Nusantara University

24. At any one time, the machine is in one particular state.
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.
Bina Nusantara University

25. Using FSM to Solve Problem
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.
Bina Nusantara University

26. State Diagram of Vending Machine
Bina Nusantara University

27. Summary
Knowledge representation is of major importance in expert systems.
A number of different KR techniques have been devised. These include rules, semantic nets, frames, script, logic, conceptual graphs, and others.
Semantic network is a classic AI representation technique used for propositional information.
Semantic nets are easy to translate into PROLOG.
There are some difficulties with semantic nets.
Schemata is used to describe a more complex knowledge structure than the semantic net.
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28. Exercise
Draw semantic net for computers using AKO and IS-A links. Consider the classes of microcomputer, mainframe, supercomputer, computing systems, dedicated, general purpose, board-level, computer-on-a-chip, single processor, and multiprocessor. Include specific instances.
Draw a semantic net for computer communications using AKO and IS-A links. Consider the classes of local area net, wide area net, token ring, star, centralized, decentralized, distributed, modems, telecommunications, newsgroups, and electronic mail. Include specific instances.
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29. References
Joseph Giarratano, Gary Riley Expert Systems: Principles and Programming Chapter 2. Thomson Course Technology. Australia. ISBN:
Peter Jackson Introduction to Expert Systems. Addison-Wesley. Harlow, England. ISBN:
Marriage-and-Machine-Adventures-in-Artificial
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