1. Expert Systems Chapter 8

2. 323-670 Artificial IntelligenceChapter 82 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

3. 323-670 Artificial IntelligenceChapter 83 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.

4. 323-670 Artificial IntelligenceChapter 84 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specific characteristic of the problem 1) Brittleness ( เปราะบาง ) : no general knowledge that can be used, the data is out of date 2) Lack of meta-knowledge : the limitation of the control operation for reasoning 3) Knowledge acquisition : difficult to transform the knowledge from human to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.

5. 323-670 Artificial IntelligenceChapter 85 AI Fields Expert systems NLP Robotic Machine learning Game playing Computer vision

6. 323-670 Artificial IntelligenceChapter 86 Knowledge Definitions a clear and certain perception of thing understanding learning skill recognition organized information applicable to problem solving

7. 323-670 Artificial IntelligenceChapter 87 Abstraction of Knowledge

8. 323-670 Artificial IntelligenceChapter 88 Knowledge Base To buy a new car.............

9. 323-670 Artificial IntelligenceChapter 89 Problem Reduction Analysis Shopping Financing

10. 323-670 Artificial IntelligenceChapter 810 Block world Problem Find a search Tree How to generate all moves initial state  goal state

11. 323-670 Artificial IntelligenceChapter 811 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

12. 323-670 Artificial IntelligenceChapter 812 Conventional System and ES

13. 323-670 Artificial IntelligenceChapter 813 Categories of ES Interpretation Prediction Diagnosis Design Planning Monitoring Debugging Repair Instruction Control

14. 323-670 Artificial IntelligenceChapter 814 Knowledge in the KB

15. 323-670 Artificial IntelligenceChapter 815 Structure of ES 2 parts –consultation –development Knowledge Engineer Expert knowledge Knowledge Base –Facts –Rules Explanation 1 2

16. 323-670 Artificial IntelligenceChapter 816 Knowledge Engineer

17. 323-670 Artificial IntelligenceChapter 817 Knowledge Engineer Process BOOK RULES

18. 323-670 Artificial IntelligenceChapter 818 Knowledge Acquisition

19. 323-670 Artificial IntelligenceChapter 819 Knowledge Acquisition Methods

20. 323-670 Artificial IntelligenceChapter 820 Knowledge Engineer

21. 323-670 Artificial IntelligenceChapter 821 Semantic Network

22. 323-670 Artificial IntelligenceChapter 822 Validation

23. 323-670 Artificial IntelligenceChapter 823 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).

24. 323-670 Artificial IntelligenceChapter 824 EX18EX01.pro : Animal 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). goal: run

25. 323-670 Artificial IntelligenceChapter 825 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).

26. 323-670 Artificial IntelligenceChapter 826 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). EX18EX01.pro : Animal (cont.)

27. 323-670 Artificial IntelligenceChapter 827 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. EX18EX01.pro : Animal (cont.)

29. 323-670 Artificial IntelligenceChapter 829

30. 323-670 Artificial IntelligenceChapter 830 The End