CSNB234 ARTIFICIAL INTELLIGENCE

1. CSNB234ARTIFICIAL INTELLIGENCE Chapter 7 Introduction to Expert Systems (Chapter 8, Textbook) (Chapter 3 & Chapter 6, Ref. #1) Instructor: Alicia Tang Y. C.

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. An “expert system” is defined as“A computerized clone of a human expert” (Definition taken from Oxford Science Publication)

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

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

7. 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 (i.e. it is algorithmic) 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 (this is called fast prototyping) changes in the rules are easy to accomplish Comparison (I)

8. 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 heuristic based on rules of logic E___________ is the major goal easily deal with q______ data Comparison (II)

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.

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. 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 Other categories include: monitoring, debugging, control, instruction

12. BENEFITS OF EXPERT SYSTEMS (I) • Expertise in a field is made available to many more people (even when human experts are not around in the company). • Top experts’ knowledge gets saved rather than being lost, when they retire or should they have resigned. • Facts are stored in a “Systematic” way. • & Easy to keep on adding new knowledge on it • Allows human experts to handle more complex problems rapidly and reliably.

13. Early EXPERT SYSTEMS (70s – mid 80s) • MYCIN • USES RULE-BASED SYSTEM • GOAL-DRIVEN • RULES INCORPORATED IN MYCIN REFLECTED UNCERTAINTY ASSOCIATED WITH KNOWLEDGE • CERTAINTY FACTOR WAS USED TO DERIVE CONCLUSION • DENTRAL • WAS DEVELOPED IN STANFORD UNIVERSITY TO ANALYZE AND INTERPRET CHEMICALS AND THEIR MOLECULAR STRUCTURES • DEVELOPERS INCLUDE JOSHUA LEDERBERG (NOBEL PROZE WINNER IN GENETICS) • EXPERT’S “KNOW-HOW” ARE EXPRESSED IN RULES; RULE-OF-THUMB TECHNIQUE IS USED • PROSPECTOR • KBS TO INTERPRET GEOLOGIC DATA FOR MINERALS EXPLORATION • INCOPORATED BAYES THEOREM (PROBABILISTIC REASONING APPROACH)

14. Early EXPERT SYSTEMS (70s – mid 80s) • XCON • RULE-BASED SYSTEM, DATA-DRIVEN • REVEAL • FUZZY LOGIC USED • CENTAUR • RULES AND FRAMES-BASED SYSTEM • HEARSAY I • – for speech recognition

15. Characteristics common to early ES • Could perform at a level equivalent to human experts • Large amount of domain specific knowledge • Rule-based systems: knowledge incorporated in the form of production rule

16. Popular Expert Systems Application Domain • Electronics : helps in VLSI/ULSI design • Law : system serves as an auditor • Manufacturing : inproduction & process controls • Medicine : illness diagnosis • Chemistry : synthesis planning

17. EXPERT SYSTEMS:LIMITATIONS • SYSTEMS ARE TOO SUPERFICIAL • RAPID DEGRADATION OF PERFORMANCE • INTERFACES ARE STILL CRUDE • INABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING • E.g. either forward or backward and not both

18. The Resistors • Domain experts • Non-experts • Other information technologists such as DBA, Network specialists • Users • Management • Troublemakers

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

20. Figure: Key components of an Expert Systems

21. 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

22. Importance of Explanation • It can influence the ultimate acceptance of an Expert System. • Use as a debugging tool. • Use as a component of a tutoring system. Who needs explanation? Our clients : to be convinced to purchase. Knowledge Engineer: to check if all specifications are met?

23. 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

24. Approaches Used (2) • 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

25. 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.

26. FACTS AND RULES (revision) • FACTS : • A mammal is an animal • A bird is an animal • Arthur is a man • Ben drives a car • Catherine has blue eyes • 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.

27. Examples of rules: Rule 1: if you work hard and smart then you will pass all examinations Rule 2: if the food is good then give tips to the waiter Rule 3: if a person has US1,000,000 then he is a millionaire

28. Forward Chaining and Backward Chaining 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.

29. 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 Chaining Systems

30. Inference Strategies (I) Conclusion (Goals) Input Data Few Items (For Example, User Specifications for a Computer System) Many Possibilities (For Example, a Computer Configuration) (a) Forward Chaining: IF - Part Matches Shown

31. Inference Strategies (II) Input Data Conclusion (Goals) Extensive; Much of the Data Obtained by the System Querying the User (For Example, Investor’s Profile) Few Possibilities (Known in Advance ((For Example, Investment Options) (b) Backward Chaining: THEN - Part Matches Shown

32. 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 considered ‘too difficult’.

33. Nice to know…

34. 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

35. 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.

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

37. SUBSUMED RULES • Rule 1 • if A = X AND B= Y THEN C = Z • Rule 2 • if A=X THEN C=Z • to be revised.

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

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