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Chapter 8. Expert Systems. Expert System. p. 547 MYCIN (1976) see section 8.2 backward chaining + certainty factor and rule-based systems p.233 Bayesian network p. 239 Fuzzy logic p. 246 Probability and Bayes ’ theorem p. 231 PROSPECTOR (1976), DENDRAL (1978)

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Expert Systems


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    1. Chapter 8 Expert Systems

    2. Expert System p. 547 MYCIN (1976) see section 8.2 backward chaining + certainty factor and rule-based systems p.233 Bayesian network p. 239 Fuzzy logic p. 246 Probability and Bayes’ theorem p. 231 PROSPECTOR (1976), DENDRAL (1978) expert systems shells EMYCIN Chapter 8

    3. Expert System using domain knowledge knowledge representation p. 297 reasoning with the knowledge, explanation Knowledge acquisition (p. 553) 1) entering knowledge 2) maintaining knowledge base consistency 3) ensuring knowledge base completeness MOLE (1988) is a knowledge acquisition system for heuristic classification problems such as diagnosing diseases. Chapter 8

    4. Expert System problem :the number of rules may be large control structure depend on the specific characteristic of the problem 1) Brittleness (เปราะบาง): no general knowledge that can be used, the data is out of date 2) Lack of meta-knowledge : the limitation of the control operation for reasoning 3) Knowledge acquisition : difficult to transform the knowledge from human to machine 4) Validation : the correctness of the knowledge in the system, no formal proof that machine is better than human or human better than machine. Chapter 8

    5. AI Fields • Expert systems • NLP • Robotic • Machine learning • Game playing • Computer vision Chapter 8

    6. Knowledge Definitions • a clear and certain perception of thing • understanding • learning • skill • recognition • organized information applicable to problem solving Chapter 8

    7. Abstraction of Knowledge Chapter 8

    8. Knowledge Base To buy a new car............. Chapter 8

    9. Problem Reduction • Analysis • Shopping • Financing Chapter 8

    10. Block world Problem • Find a search Tree • How to generate all moves • initial state  goal state Chapter 8

    11. Expert Systems Definition • Expert systems (ES) is a system that employs human knowledge captured in a computer to solve problems that ordinary require human expertise. • ES uses by expert as knowledgeable assistance. • Specific domain Chapter 8

    12. Conventional System and ES Chapter 8

    13. Categories of ES • Interpretation • Prediction • Diagnosis • Design • Planning • Monitoring • Debugging • Repair • Instruction • Control Chapter 8

    14. Knowledge in the KB Chapter 8

    15. 1 2 Structure of ES • 2 parts • consultation • development • Knowledge Engineer • Expert knowledge • Knowledge Base • Facts • Rules • Explanation Chapter 8

    16. Knowledge Engineer Chapter 8

    17. Knowledge Engineer Process BOOK RULES Chapter 8

    18. Knowledge Acquisition Chapter 8

    19. Knowledge Acquisition Methods Chapter 8

    20. Knowledge Engineer Chapter 8

    21. Semantic Network Chapter 8

    22. Validation Chapter 8

    23. EX05EX14.PRO :Guess a number predicates action(integer) clauses action(1) :- !, write("You typed 1."). action(2) :- !, write("You typed two."). action(3) :- !, write("Three was what you typed."). action(_) :- !, write("I don't know that number!"). goal write("Type a number from 1 to 3: "), readreal(Choice), action(Choice). Chapter 8

    24. EX18EX01.pro : Animal goal: run predicates animal_is(symbol) it_is(symbol) ask(symbol, symbol, symbol) positive(symbol, symbol) negative(symbol, symbol) clear_facts run clauses animal_is(cheetah) :- it_is(mammal), it_is(carnivore), positive(has, tawny_color), positive(has, dark_spots). animal_is(tiger) :- it_is(mammal), it_is(carnivore), positive(has, tawny_color), positive(has, black_stripes). Chapter 8

    25. EX18EX01.pro : Animal (cont.) animal_is(giraffe) :- it_is(ungulate), positive(has, long_neck), positive(has, long_legs), positive(has, dark_spots). animal_is(zebra) :- it_is(ungulate), positive(has,black_stripes). animal_is(ostrich) :- it_is(bird), negative(does, fly), positive(has, long_neck), positive(has, long_legs), positive(has, black_and_white_color). animal_is(penguin) :- it_is(bird), negative(does, fly), positive(does, swim), positive(has, black_and_white_color). animal_is(albatross) :- it_is(bird), positive(does, fly_well). Chapter 8

    26. EX18EX01.pro : Animal (cont.) it_is(mammal) :- positive(has, hair). it_is(mammal) :- positive(does, give_milk). it_is(bird) :- positive(has, feathers). it_is(bird) :- positive(does, fly), positive(does,lay_eggs). it_is(carnivore) :- positive(does, eat_meat). it_is(carnivore) :-positive(has, pointed_teeth), positive(has, claws), positive(has, forward_eyes). it_is(ungulate) :- it_is(mammal), positive(has, hooves). it_is(ungulate) :- it_is(mammal), positive(does, chew_cud). positive(X, Y) :- ask(X, Y, yes). negative(X, Y) :- ask(X, Y, no). Chapter 8

    27. EX18EX01.pro : Animal (cont.) ask(X, Y, yes) :- !, write(“Question > “, X, " it ", Y, “?”,’ \n’), readln(Reply), frontchar(Reply, 'y', _). ask(X, Y, no) :- !, write(“Question > “,X, " it ", Y, “?”,’\n’), readln(Reply), frontchar(Reply, 'n', _). clear_facts :- write("\n\nPlease press the space bar to exit\n"), readchar(_). run :- animal_is(X), !, write("\nAnswer.... => Your animal may be a (an) ",X), nl, nl, clear_facts. run :- write("\n Answer.... => Unable to determine what"), write("your animal is.\n\n"), clear_facts. Chapter 8

    28. Chapter 8

    29. The End Chapter 8