1. CHAPTER 12 Knowledge Representation

2. Once acquired, knowledge must be organized for use

3. Introduction n A good knowledge representation naturally represents the problem domain n An unintelligible knowledge representation is wrong n Most artificial intelligence systems consist of: – Knowledge Base – Inference Mechanism (Engine)

4. n Knowledge Base – Forms the system's intelligence source – Inference mechanism uses to reason and draw conclusions n Inference mechanism: Examines the knowledge base to answer questions, solve problems or make decisions within the domain

5. n Many knowledge representation schemes – Can be programmed and stored in memory – Are designed for use in reasoning n Major knowledge representation schemas: – Production rules – Frames

6. Representation in Logic and Other Schemas n General form of any logical process n Inputs (Premises) n Premises used by the logical process to create the output, consisting of conclusions (inferences) n Facts known true can be used to derive new facts that are true

7. n Symbolic logic : System of rules and procedures that permits the drawing of inferences from various premises n Basic Forms of Computational Logic – Propositional logic (or propositional calculus) – Predicate logic (or predicate calculus)

8. Propositional Logic n A proposition is a statement that is either true or false n Once known, it becomes a premise that can be used to derive new propositions or inferences n Rules are used to determine the truth (T) or falsity (F) of the new proposition

9. n Symbols represent propositions, premises or conclusions Statement: A = The mail carrier comes Monday through Friday. Statement: B = Today is Sunday. Conclusion: C = The mail carrier will not come today. n Propositional logic: limited in representing real-world knowledge

10. Predicate Calculus n Predicate logic breaks a statement down into component parts, an object, object characteristic or some object assertion n Predicate calculus uses variables and functions of variables in a symbolic logic statement n Predicate calculus is the basis for Prolog (PROgramming in LOGic) n Prolog Statement Examples – comes_on(mail_carrier, monday). – likes(jay, chocolate). (Note - the period “. ” is part of the statement)

11. Scripts Knowledge Representation Scheme Describing a Sequence of Events n Elements include – Entry Conditions – Props – Roles – Tracks – Scenes

12. Lists Written Series of Related Items n Normally used to represent hierarchical knowledge where objects are grouped, categorized or graded according to – Rank or – Relationship

13. Decision Tables (Induction Table) Knowledge Organized in a Spreadsheet Format n Attribute List n Conclusion List n Different attribute configurations are matched against the conclusion

14. Decision Trees n Related to tables n Similar to decision trees in decision theory n Can simplify the knowledge acquisition process n Knowledge diagramming - very natural

15. O-A-V Triplet n Objects, Attributes and Values n O-A-V Triplet n Objects may be physical or conceptual n Attributes are the characteristics of the objects n Values are specific measures of the attributes

17. n Deals with uncertainties n Incomplete information

18. Knowledge Maps n Visual representation n Cognitive maps

19. Semantic Networks n Graphic Depiction of Knowledge n Nodes and Links Showing Hierarchical Relationships Between Objects n Nodes: Objects n Arcs: Relationships – is-a – has-a

20. n Semantic networks can show inheritance n Semantic Nets - visual representation of relationships n Can be combined with other representation methods

21. Semantic Network Example Joe Boy Kay Woman Food Human Being School Has a child Needs Goes to Is a Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22. Production Rules n Condition-Action Pairs – IF this condition (or premise or antecedent) occurs, – THEN some action (or result, or conclusion, or consequence) will (or should) occur – IF the stop light is red AND you have stopped, THEN a right turn is OK

23. n Each production rule in a knowledge base represents an autonomous chunk of expertise n When combined and fed to the inference engine, the set of rules behaves synergistically n Rules can be viewed as a simulation of the cognitive behavior of human experts n Rules represent a model of actual human behavior

24. Forms of Rules n IF premise, THEN conclusion – IF your income is high, THEN your chance of being audited by the IRS is high n Conclusion, IF premise – Your chance of being audited is high, IF your income is high

25. n Inclusion of ELSE – IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, OR ELSE your chance of being audited is low n More Complex Rules – IF credit rating is high AND salary is more than $30,000, OR assets are more than $75,000, AND pay history is not "poor," THEN approve a loan up to $10,000, and list the loan in category "B. ” – Action part may have more information: THEN "approve the loan" and "refer to an agent"

26. Knowledge and Inference Rules Common Types of Rules n Knowledge rules, or declarative rules, state all the facts and relationships about a problem n Inference rules, or procedural rules, advise on how to solve a problem, given that certain facts are known n Inference rules contain rules about rules (metarules) n Knowledge rules are stored in the knowledge base n Inference rules become part of the inference engine

27. Advantages of Rules n Easy to understand (natural form of knowledge) n Easy to derive inference and explanations n Easy to modify and maintain n Easy to combine with uncertainty n Rules are frequently independent

28. n Complex knowledge requires many rules n Builders like rules (hammer syndrome) n Search limitations in systems with many rules Limitations of Rules

30. Frames Definitions and Overview n Frame: Data structure that includes all the knowledge about a particular object n Knowledge organized in a hierarchy for diagnosis of knowledge independence n Form of object-oriented programming for AI and ES. n Each Frame Describes One Object n Special Terminology

32. n Concise, natural, structural representation of knowledge n Encompasses complex objects, entire situations or a management problem as a single entity n Frame knowledge is partitioned into slots n Slot can describe declarative knowledge or procedural knowledge n Major Capabilities of Frames n Typical frame describing an automobile n Hierarchy of Frames: Inheritance

34. Multiple Knowledge Representations n Rules + Frames n Others Knowledge Representation Must Support n Acquiring knowledge n Retrieving knowledge n Reasoning

35. Considerations for Evaluating a Knowledge Representation n Naturalness, uniformity and understandability n Degree to which knowledge is explicit (declarative) or embedded in procedural code n Modularity and flexibility of the knowledge base n Efficiency of knowledge retrieval and the heuristic power of the inference procedure

36. n No single knowledge representation method is ideally suited by itself for all tasks n Multiple knowledge representations: each tailored to a different subtask n Production Rules and Frames works well in practice n Object-Oriented Knowledge Representations – Hypermedia

37. Experimental Knowledge Representations n Cyc n NKRL n Spec-Charts Language

38. The Cyc System n Attempt to represent a substantial amount of common sense knowledge n Bold assumptions: intelligence needs a large amount of knowledge n Need a large knowledge base n Cyc over time is developing as a repository of a consensus reality - the background knowledge possessed by a typical U.S. resident n There are some commercial applications based on portions of Cyc

39. NKRL n Narrative Knowledge Representational Language (NKRL) n Standard, language-independent description of the content of narrative textual documents n Can translate natural language expressions directly into a meaningful set of templates that represent the knowledge

40. Knowledge Interchange Format (KIF) To Share Knowledge and Interact

41. The Spec-Charts Language n Based on Conceptual Graphs: to Define Objects and Relationships n Restricted Form of Semantic Networks n Evolved into the Commercial Product - STATEMATE

42. Knowledge Representation and the Internet n Hypermedia documents to encode knowledge directly n Hyperlinks Represent Relationships n MIKE (Model-based and Incremental Knowledge Engineering n Formal model of expertise: KARL Specification Language n Semantic networks: Ideally suited for hypermedia representation n Web-based Distributed Expert System (Ex-W-Pert System) for sharing knowledge-based systems and groupware development

43. Representing Uncertainty: An Overview Dealing with Degrees of Truth, Degrees of Falseness in ES n Uncertainty – When a user cannot provide a definite answer – Imprecise knowledge – Incomplete information

44. Several Approaches Related to Mathematical and Statistical Theories n Bayesian Statistics n Dempster and Shafer's Belief Functions n Fuzzy Sets Uncertainty

45. Uncertainty in AI Approximate Reasoning, Inexact Reasoning

46. Relevant Information is Deficient in One or More n Information is partial n Information is not fully reliable n Representation language is inherently imprecise n Information comes from multiple sources and it is conflicting n Information is approximate n Non-absolute cause-effect relationships exist n Can include probability in the rules n IF the interest rate is increasing, THEN the price of stocks will decline (80% probability)