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CCSB354 ARTIFICIAL INTELLIGENCE

CCSB354 ARTIFICIAL INTELLIGENCE. Chapter 7 Introduction to Expert Systems. (Chapter 8, Textbook) (Chapter 3 & Chapter 6, Ref. #1). Instructor: Alicia Tang Y. C. EXPERT SYSTEM (ES). Definition

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CCSB354 ARTIFICIAL INTELLIGENCE

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  1. CCSB354ARTIFICIAL 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. From Oxford Science Publication An expert system is defined as“a computerized clone of a human expert”

  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 changes in programs are tedious Expert Systems knowledge base is separated from the processing (inference) mechanism program may make mistake (we want it to make mistake!) the system can operate with only a few rules (as the first prototype) execution is done by using heuristic and logic changes in the rules are easy to accomplish Comparison (I)

  8. Conventional Systems do not usually explain why or how conclusions were drawn need complete information to operate Efficiency is a major goal easily deal with quantitative data Expert Systems explanation is part of most ES can operate with incomplete or uncertain information Effective is the major goal easily deal with quantitative data Comparison (II)

  9. RIGHT TASKS FOR RIGHT SYSTEMS • Facts that are known • Expertise available but is expensive • Those involved • Analyzing large/diverse data • E.g. Production scheduling & planning, diagnosing and troubleshooting, etc.

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

  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 • Debugging, Monitoring & control, Instruction

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

  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. LIMITATIONS • Systems are superficial • Rapid degradation of performance • Interfaces are crude (not impressive) • No GUI • Inability to adapt to more than one type of reasoning (in most cases)

  15. TYPICAL 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)

  16. Another view: Key components of an Expert Systems

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

  18. Importance of Explanation • It can influence the ultimate acceptance of an Expert System. • It can serve as a debugging tool. • It is used as a tutoring system for new knowledge engineer in a project team. • Who needs explanation? • our clients: • in order to convince them to buy. • the knowledge engineer: • to check if all specifications are met?

  19. Approaches Used (1) • Canned Text • Prepared in advance all questions and answers as text strings • When a question is asked, the system will find explanation module and displays the corresponding answer • Problem: • Difficult to secure consistency when the KB size grows bigger • It is only suitable for slow changing system

  20. Approaches Used (2) • Paraphrase • Tree Traversing is used • For examples, to answer “why..’ and ‘how..’ types of question • The explanation module will look up a search tree to answer “WHY this happened..” • The explanation module will look down the tree to see sub goals that were satisfied to achieve the goal state

  21. Rule-based Systems Recall that: Most expert systems are rule-based.

  22. FACTS AND RULES (revisited) • 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. 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

  24. Forward Chaining and Backward Chaining

  25. Forward chaining it is used to predict the outcome from various facts Facts are conditions in a rule It is also used to find a suitable goal that all factors can be satisfied Backward chaining it is useful when trying to determine the reason once something has occurred It is used when the result for a problem is already found, and we want to know how Reasoning (Chaining) Systems

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

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

  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. For your information…supplementary topic

  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. 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. 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.

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

  34. 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.

  35. 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 !

  36. Example Select Auto is an expert system designed to assist a user to make a right decision of buying a new car. It will review prospective cars that match with users’ need and preference

  37. The car is made in • 1. the United State • 2. foreign countries • 3. Don’t know • 2 • Quality is • 1. the highest concern • 2. of high concern • 3. of moderate concern • 4. Don’t know • 1

  38. Price of the car is • 1. important • 2. unimportant • 3. don’t know • WHY • RULE NUMBER: 5 IF • Price of a car is important and (2) The payment is in installments THEN The monthly payment is determined

  39. Price of the car is • 1. important • 2. unimportant • 3. don’t know • 1 • The monthly payment is no more than • 1. $100 • 2. $150 • 3. $200 • 4. $250 • 4

  40. The most considered factor in making a decision to buy a car is • 1. Price • 2. Fuel economy • 3. quality • 1, 3

  41. Result • Values based on -100 to +100 system VALUE • Toyota Corolla 51 • Proton Perdana 43

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