1. INFO612 Knowledge-Based Systems Dr. R. Weber

2. Copyright R. Weber Expert Systems Expert Systems are the first successful knowledge-based methodology uses knowledge ( in its knowledge base ) & reasoning Systems that manipulate knowledge and reasoning to solve problems rationally. KBS, Knowledge engineering, ES

3. Copyright R. Weber Expert Systems, what and what not ES use knowledge and inference procedures to solve problems that are difficult enough to require human expertise to solve (Feigenbaum, 82) ES is a methodology to develop computer programs that manipulates expertise to solve problems that require human expertise in restricted domains (Weber 02)

4. Copyright R. Weber Expert Systems: history (i)

5. Copyright R. Weber Expert Systems: history (ii)

6. Copyright R. Weber Expert Systems: domain areas agriculture, business, chemistry, communications, computer systems, education, electronics, engineering, environment, geology, law, manufacturing, mathematics, medicine, simulation, transportation, etc.

7. Copyright R. Weber Expert Systems: when ES are indicated to solve expert problems in restricted domains without an efficient algorithmic solution is there an alternative method? ill-structured problems is the domain well-bounded? how available is the source of knowledge? is the approach to the problem it trial-and-error? is the approach to the problem heuristic?

8. Copyright R. Weber Expert Systems: tasks analysis, configuration, control, design, diagnosis, instruction, interpretation, monitoring, planning, prediction, prescription, prognosis, remedy, selection and simulation.

9. Copyright R. Weber ES and AI tasks From: Durkin, J. (1994). Expert Systems: design and development. Prentice-Hall, Inc., New Jersey.

10. Copyright R. Weber Types of AI tasks mundane:  face recognition  argumentation  shopping planning expert:  diet prescription  medical diagnosis  legal argumentation  legal, military, business planning

11. Copyright R. Weber the concept knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) expert problem inference engine (agenda) inference engine (agenda) expert solution knowledge reasoning

12. Copyright R. Weber expert solution The complete methodology knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) explanation general knowledge user I n t e r f a c e user I n t e r f a c e expert problem inference engine (agenda) inference engine (agenda) working memory ( short-term mem/information ) working memory ( short-term mem/information ) Knowledge acquisition

13. Copyright R. Weber What are rule-based expert systems?

14. Copyright R. Weber Represent knowledge? How ES represent knowledge? Knowledge representation formalisms associated to ES: rules semantic networks frames logic

15. Copyright R. Weber (Production) Rules A logic sequence of an antecedent (premise, condition) and a consequence (conclusion, action). Both antecedent and conclusion are, in essence, facts.

16. Copyright R. Weber (Production) Rules (ii) The antecedent attempts to verify if the fact is true or false, when the fact composing the antecedent is true, the conclusion is triggered. The antecedent can be composed of several facts connected through operators such as and, or, and not. Conclusions usually change or assign values to attributes of an object, call methods or trigger other rules.

17. Copyright R. Weber Semantic Networks (SN) commonly used in logic-based expert systems directed graphs where: nodes represent objects and concepts arcs represent relationships between objects and attributes Quillian, 1968 used to represent static elements of a representation such as the class, the instances and its features

18. Copyright R. Weber Characteristics of SN cannot represent all magnitude of data (meal varying from sandwich to 20 course meal) very restricted in terms of inferencing only inheritance through instance and subclass convenient when mathematical algorithms are applied over knowledge because graphs also provide a formal and precise representation model

19. Copyright R. Weber Example of Semantic Networks

20. Copyright R. Weber Frames representation formalism commonly used in expert systems represents declarative, structural and procedural knowledge

21. Copyright R. Weber Frames Introduced by M. Minski in 1975, “A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed”.

22. Copyright R. Weber Concepts, Objects and Facts An object is a basic entity that can be instantiated. A concept tells something about the object. A concept can be represented as an abstraction of an object when several objects can be grouped under the same concept (e.g., client 1, client 2, all clients);

23. Copyright R. Weber Concepts, Objects and Facts (ii) or a concept can be an attribute, when it tells something exclusively about this object or due to the analysis it is not worthy to represent it as an abstraction. When an object is associated to a valued attribute, it is a fact. A fact is a statement that can be either true or false (Durkin, 1994). Concepts can be described in a computer program via Y/N or T/F statements.

24. Copyright R. Weber Characteristics of frames support inheritance (subclasses and instances) support methods  when needed  after changed  before changed easy to implement in different programming paradigms, logic-based or not

25. Copyright R. Weber Rules combined with frames advantages  faster inferences, increases inferential efficiency rules with variables in its antecedents and conclusions

26. Copyright R. Weber Decision trees Knowledge representation formalism Represent mutually exclusive rules (disjunction) A way of breaking up a data set into classes or categories

27. Copyright R. Weber Decision trees consist of: - leaf nodes (classes) - decision nodes (tests on attribute values) - from decision nodes branches grow for each possible outcome of the test From Cawsey, 1997

28. Copyright R. Weber Knowledge representation formalisms rules logic concepts, frames semantic nets decision trees representational adequacy inferential adequacy inferential efficiency clear syntax and semantics naturalness

29. Copyright R. Weber Inference engine forward chaining backward chaining logic theory

30. Copyright R. Weber Investment Advisor Frames Concepts, Objects, Facts Rules Backward Chaining

31. Copyright R. Weber Heart Attack Triage Facts/Predicates Rules Forward Chaining

32. Copyright R. Weber Clips Rule-based Forward chaining Logic-based

33. Copyright R. Weber Clips rules or productions

34. Copyright R. Weber Compound productions

35. Copyright R. Weber Expert Systems: requirements high quality: system must perform equally or better than a human expert response time should be adequate to the problem it solves reliable: not prone to crashes & errors explanation capability should be present with the purpose of justification and verification of correctness (p. 9,10 for explanation styles) flexible: supported by good maintenance methods

36. Copyright R. Weber Advantages (i) Permanence of knowledge: Expert systems do not forget or retire or quit, but human experts may Breadth: One ES can entail knowledge learned from an unlimited number of human experts. Reproducibility: Many copies of an expert system can be made, but training new human experts is time-consuming and expensive. Efficiency: can increase throughput and decrease personnel costs Differentiation: In some cases, an expert system can differentiate a product or can be related to the focus of the firm Cost: Although expert systems are expensive to build and maintain, they are inexpensive to operate. Development and maintenance costs can be spread over many users. Cost savings, e.g., wages, minimize loan loss, reduce customer support effort. The overall cost can be quite reasonable when compared to expensive and scarce human experts

37. Copyright R. Weber Advantages (ii) Documentation - An expert system can provide permanent documentation of the decision process Increased availability: the mass production of expertise Completeness - An expert system can review all the transactions, a human expert can only review a sample; an ES solution will always be complete and deterministic Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making Consistency - With expert systems similar transactions handled in the same way. Humans are influenced by recency effects and primacy effects (early information dominates the judgment). Entry barriers - Expert systems can help a firm create entry barriers for potential competitors

38. Copyright R. Weber Advantages (iii) Computer programs are best in those situations where there is a structure that is noted as previously existing or can be elicited Reduced danger: ES can be used in any environment Reliability: ES will keep working properly regardless of of external conditions that may cause stress to humans Explanation: ES can trace back their reasoning providing justification, increasing the confidence that the correct decision was made Domain analysis: Indirect advantage is that the development of an ES requires that knowledge and processes are verified for correctness, completeness, and consistency. If there is a maze of rules (e.g. tax and auditing or laws), then the expert system can "unravel" the maze Maintenance: only knowledge base can be modified, without interference to other modules of the program

39. Copyright R. Weber Disadvantages of Rule-based ES (i) Common sense - In addition to a great deal of technical knowledge, human experts have common sense. To program common sense in an ES, you must acquire and represent rules. Creativity - Human experts can respond creatively to unusual situations, expert systems cannot. Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated. Complexity and interrelations of rules grow exponentially as more rules are added.

40. Copyright R. Weber Degradation - Expert systems are not good at recognizing when no answer exists or when the problem is outside their area of expertise. So, ES may provide a solution that is not optimal like one that is optimal High knowledge engineering requirements: In many real world domains, the amount of knowledge necessary to cover an expert problem is abundant making ES development time-consuming and complex Knowledge acquisition bottleneck Difficulty to deal with imprecision (I.e., incompleteness,, uncertainty, ignorance, ambiguity) poses an extra engineering requirement; treatments of imprecision also have to be represented Disadvantages of Rule-based ES (ii)

41. Copyright R. Weber Necessary grounds for computer understanding Ability to represent knowledge and reason with it. Perceive equivalences and analogies between two different representations of the same entity/situation. Learning and reorganizing new knowledge.  From Peter Jackson (1998) Introduction to Expert systems. Addison- Wesley third edition. Chapter 2, page 27.