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Chapter 5

Chapter 5. Expert Systems. AI Fields. Expert systems NLP Robotic Machine learning Game playing Computer vision. Knowledge Definitions. a clear and certain perception of thing understanding learning skill recognition organized information applicable to problem solving.

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Chapter 5

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  1. Chapter 5 Expert Systems

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

  3. Knowledge Definitions • a clear and certain perception of thing • understanding • learning • skill • recognition • organized information applicable to problem solving Chapter 5

  4. Abstraction of Knowledge Chapter 5

  5. Knowledge Base To buy a new car............. Chapter 5

  6. Problem Reduction • Analysis • Shopping • Financing Chapter 5

  7. Block world Problem • Find a search Tree • How to generate all moves • initial state  goal state Chapter 5

  8. 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 5

  9. Conventional System and ES Chapter 5

  10. Categories of ES • Interpretation • Prediction • Diagnosis • Design • Planning • Monitoring • Debugging • Repair • Instruction • Control Chapter 5

  11. Knowledge in the KB Chapter 5

  12. 1 2 Structure of ES • 2 parts • consultation • development • Knowledge Engineer • Expert knowledge • Knowledge Base • Facts • Rules • Explanation Chapter 5

  13. Knowledge Engineer Chapter 5

  14. Knowledge Engineer Process BOOK RULES Chapter 5

  15. Knowledge Acquisition Chapter 5

  16. Knowledge Acquisition Methods Chapter 5

  17. Knowledge Engineer Chapter 5

  18. Semantic Network Chapter 5

  19. Validation Chapter 5

  20. 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 5

  21. 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 5

  22. 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 5

  23. 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 5

  24. 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 5

  25. Natural Language Processing Sentence :- Noun_phrase, Verb_phrase. Noun_phrase :- Det, Noun. Noun_phrase :- Noun. Verb_phrase :- Verb, Noun_phrase. Verb_phrase :- verb. EX : The cat eats the fish. A man likes an apple. Chapter 5

  26. EX13EX04.pro  NLP.pro domains sentence = s(noun_phrase,verb_phrase) noun_phrase = noun(noun) ; noun_phrase(detrm,noun) noun = string verb_phrase = verb(verb) ; verb_phrase(verb,noun_phrase) verb = string detrm = string predicates s_sentence(string,sentence) s_noun_phrase(string,string,noun_phrase) s_verb_phrase(string,verb_phrase) d(string) n(string) v(string) start goal start. goal: Please enter the sentence > Bill eats apple Chapter 5

  27. EX13EX04.pro  NLP.pro (cont) clauses start :- write("\n Please enter a sentence > "), readln(Str), s_sentence(Str,s(_,_)). s_sentence(Str, s(N_Phrase,V_Phrase) ):- s_noun_phrase(Str, Rest, N_Phrase), s_verb_phrase(Rest, V_Phrase). s_noun_phrase(Str, Rest, noun_phrase(Detr,Noun)):- fronttoken(Str,Detr,Rest1), d(Detr), fronttoken(Rest1,Noun,Rest), n(Noun). s_noun_phrase(Str,Rest,noun(Noun)):- fronttoken(STR,Noun,Rest), n(Noun). s_verb_phrase(Str, verb_phrase(Verb,N_Phrase)):- fronttoken(Str,Verb,Rest1), v(Verb), s_noun_phrase(Rest1,"",N_Phrase). s_verb_phrase(Str,verb(Verb)):- fronttoken(STR,Verb,""), v(Verb). Chapter 5

  28. EX13EX04.pro  NLP.pro (cont) The cat likes fish A man takes a bus /* determiner */ d("the"). d("a"). d("an"). /* nouns */ n(“Bill"). n("dog"). n("cat"). n("fish"). n("ant"). n("apple"). n("man"). n("bus"). /* verbs */ v("is"). v("eats"). v("likes"). v("takes"). Chapter 5

  29. EXPERT SYSTEMShttp://www.doctordiag.com/ นายแพทย์สุรเกียรติ อาชานานุภาพ Chapter 5

  30. Chapter 5

  31. วินิจฉัยโรค Chapter 5

  32. answer Chapter 5

  33. ข้อมูลโรค Chapter 5

  34. ข้อมูลยา Chapter 5

  35. It is not enough to stare up the steps... We must step up the stairs.

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