1. CCSB354 ARTIFICIAL INTELLIGENCE Chapter 8 Introduction to Expert Systems Chapter 8 Introduction to Expert Systems Instructor: Alicia Tang Y. C. (Chapter 8, Textbook) (Chapter 3 & Chapter 6, Ref. #1)

2. 2 EXPERT SYSTEM (ES) Definition – ES is a set of computer programs that can advise, consult, diagnose, explain, forecast, interpret, justify, learn, plan and many more tasks that require ‘intelligence’ to perform.

3. 3 An expert system is defined as “ a computerized clone of a human expert ” From Oxford Science Publication

4. 4 CHARACTERISTICS EXPERT SYSTEMS: CHARACTERISTICS – Perform at a level equivalent to that of a human expert. – Highly domain specific. – Adequate response time – Can explain its reasoning. – It can propagate uncertainties and provide alternate solutions through probabilistic reasoning or fuzzy rules.

5. 5 AN EXPERT AND A SHELL n EXPERT: n An expert in a particular field is a person who possess considerable knowledge of his area of expertise n ES SHELL n A special purpose tool designed for certain types of applications in which user supply only the knowledge base (e.g. EMYCIN) n It isolates knowledge-bases from reasoning engine n Hence software portability can be improved Domain - specific

6. 6 Shell Concept for Building Expert Systems KB e.g. rules Consultation Manager KB Editors & debugger Explanation Program KBMF Inference Engine shell

7. 7 Comparison(I) Conventional Systems – information & its processing are combined in one sequential program – programs do not make mistake (but programmers do make it) – the system operates only when it is completed – execution is done on a step-by- step basis ( ) Expert Systems – knowledge base is separated from the processing (inference) mechanism – program may make mistake (we want it to make mistake!) – explanation is part of most ES – the system can operate with only a few rules ( ) – changes in the rules are easy to accomplish

8. 8 Comparison(II) Conventional Systems – changes in programs are tedious – do not usually explain why or how conclusions were drawn – need complete information to operate – E__________ is a major goal – easily deal with q_________ data Expert Systems – can operate with incomplete or uncertain information – execution is done by using h_________ and logic – E___________ is the major goal – easily deal with q______ data

9. 9 RIGHT TASKS FOR RIGHT SYSTEMS Facts that are known Expertise available but is expensive Analyzing large/diverse data E.g. Production scheduling & planning, diagnosing and troubleshooting, etc. (will see them later on)

10. 10 Generic Categories of Expert Systems (1) Interpretation – inferring situation descriptions from observation Prediction – inferring likely consequences of given situations Diagnosis – inferring system malfunctions from observations

11. 11 Generic Categories of Expert Systems (2) Design – configuring objects under constraints Planning – developing plans to achieve goals Repair – executing a plan to administer a prescribed remedy Others are: monitoring, debugging, control, instruction

12. 12 BENEFITS OF EXPERT SYSTEMS (I) n Expertise in a field is made available to many more people (even when human expert is not present). n Top experts’ knowledge gets saved rather than being lost, when they retire n “Systematic”; no factors forgotten. n Easy to keep on adding new knowledge n Allows human experts to handle more complex problems rapidly and reliably.

13. 13 EXAMPLES of EXPERT SYSTEMS MYCIN – USES RULE-BASED SYSTEM, GOAL-DRIVEN – EMPLOYED CF TO DERIVE CONCLUSION PROSPECTOR – INCOPORATED BAYES THEOREM (PROBABILITY) – Interpret geologic data for minerals XCON – RULE-BASED SYSTEM, DATA-DRIVEN REVEAL – FUZZY LOGIC USED CENTAUR – RULES AND FRAMES-BASED SYSTEM DENTRAL – interpret molecular structure HEARSAY I – for speech recognition

14. 14 LIMITATIONS SYSTEMS ARE TOO SUPERFICIAL RAPID DEGRADATION OF PERFORMANCE INTERFACES ARE STILL CRUDE INABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING (in most cases)

15. 15 Consultation Environment (Use) Development Environment (Knowledge Acquisition) User Expert User Interface Inference Engine Explanation Facility Working Memory Facts of the Case Recommendation, Explanation Facts of the Case Knowledge Engineer Knowledge Acquisition Facility Knowledge Base Domain Knowledge (Elements of Knowledge Base) STRUCTURE OF AN EXPERT SYSTEM

16. 16 Figure: Key components of an Expert Systems

17. 17 Explanation Facility Why need it? – It provides sound reasoning besides quality result. Common types – “ How ” a conclusion was reached – “ Why ” a particular question was asked

18. 18 Importance of Explanation It can influence the ultimate a________ of an Expert System. Use as a d______________ tool. Use as a component of a tutoring system. Who needs explanation? Clients : To be convinced. Knowledge Engineer: Specifications all met?

19. 19 Approaches Used (1) Canned Text – prepared in advance all questions and answers as text – system finds explanation module and displays the corresponding answer – problem: difficult to secure consistency – suitable for slow changing system only

20. 20 Paraphrase – Tree Traverse to answer WHY –look up the tree to answer HOW –look down the tree to see sub goals that were satisfied to achieve the goal Approaches Used (2)

21. 21 Rule-based Systems In expert system development, a tool is used to help us to make a task easier. The tool for machine thinking is the Inference Engine. Most expert systems are rule-based.

22. 22 FACTS AND RULES FACTS : A mammal is an animal A bird is an animal Adam is a man Ben drives a car RULES : If a person has RM1,000,000 then he is a millionaire. If an animal builds a nest and lays eggs then the animal is a bird.

23. 23 Rule 1:if you work hard and smart thenyou will pass all examinations Rule 2:ifthe food is good thengive tips to the waiter Rule 3:if a person has US1,000,000 thenhe is a millionaire Examples of rules:

24. 24 These are methods for deducing conclusions. The former predicts the outcome (conclusion) from various factors (conditions) while the latter could be very useful in trying to determine the causes once something has occurred. Detailed description and working examples of rule-based systems and their reasoning methods will be dealt separately in other chapters. Forward Chaining and Backward Chaining

25. 25 Chaining Systems Forward – it predicts the outcome from various factors (conditions) Backward – it could be very useful in trying to determine the cause (reason) once something has occurred

26. 26 Input Data Conclusion (Goals) Many Possibilities (a) Forward Chaining Inference Strategies (I)

27. 27 Input Data Conclusion (Goals) Few Possibilities (b) Backward Chaining Inference Strategies (II)

28. 28 Exercise #1 You have seen what tasks are “just right” for ES and now you are required to answer the following question: – List a “Too hard” task for computers and explain briefly why they are said too difficult. And, why?

29. 29 For your information… supplementary topic

30. 30 RULE-BASED VALIDATION There are essentially 5 types of inconsistency that may be identified, these are: – Redundant rules – Conflicting rule – Subsumed – Unnecessary Premise(IF) Clauses – Circular rules

31. 31 REDUNDANT RULES Rule 1 – IFA = X AND B= Y THEN C = Z Rule 2 – IF B=Y AND A=X THEN C=Z AND D=W Rule 1 is made redundant by rule 2.

32. 32 CONFLICTING RULES v Rule 1 –IF A = X AND B= Y THEN C = Z v Rule 2 –IF A=X AND B=Y THEN C=W v Rule 1 is subsumed by rule 2 thus becomes unnecessary.

33. 33 SUBSUMED RULES v Rule 1 –if A = X AND B= Y THEN C = Z v Rule 2 –if A=X THEN C=Z v to be revised.

34. 34 UNNECESSARY PREMISE (IF) CLAUSES v Rule 1 –IF A = X AND B= Y THEN C = Z v Rule 2 –IF A=X AND NOT B=Y THEN C=Z v Remove B=Y and NOT B=Y to have just one rule.

35. 35 CIRCULAR RULES v Rule 1 –IF A = X THEN B = Y v Rule 2 –IF B=Y AND C=Z THEN DECISION=YES v Rule 3 v IF DECISION=YES THEN A = X v Restructure these rules !