Rule Based Systems Chapter 12

Artificial IntelligenceChapter 82 Expert Systems 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 IntelligenceChapter 83 Expert Systems 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.

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characteristic of the problem 1) Brittleness ( เปราะบาง ) : no general knowledge that can be used, the data is out of date 2) Lack of meta-knowledge : the limitation of the control operation for reasoning 3) Knowledge acquisition : difficult to transform the knowledge from human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

Artificial IntelligenceChapter 85 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 IntelligenceChapter 86 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).

Artificial IntelligenceChapter 87 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).

Artificial IntelligenceChapter 88 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.)

Artificial IntelligenceChapter 89 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.)

Artificial IntelligenceChapter 810 The End