Chapter 8 Expert Systems Expert System p 547

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) expert systems shells EMYCIN Artificial Intelligence 2 Chapter 8

Expert System using domain 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. Artificial Intelligence 3 Chapter 8

Expert System problem : the number of rules may be large control structure depend on the specific characteristic of the problem ���� ) 1) Brittleness ( : no general knowledge that can be used, the data is out of date 2) Lack of meta-knowledge : the limitation of the control operation for reasoning 3) Knowledge acquisition : difficult to transform the knowledge from human to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. Artificial Intelligence 4 Chapter 8

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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 Artificial Intelligence 11 Chapter 8

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EX 05 EX 14. 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). Artificial Intelligence 23 Chapter 8

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EX 18 EX 01. 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). Artificial Intelligence 25 Chapter 8

EX 18 EX 01. 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). Artificial Intelligence 26 Chapter 8

EX 18 EX 01. 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("nn. Please press the space bar to exitn"), readchar(_). run : animal_is(X), !, write("n. Answer. . => Your animal may be a (an) ", X), nl, clear_facts. run : write("n Answer. . => Unable to determine what"), write("your animal is. nn"), clear_facts. Artificial Intelligence 27 Chapter 8

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The End Artificial Intelligence 30 Chapter 8