Rule Based Systems

Chapter 12

Artificial Intelligence Chapter 82

Expert Systems

p. 547 MYCIN (1976) see section 8.2

backward chaining + certainty factor and rule-based systems p.233

Bayesian network p. 239Fuzzy logic p. 246Probability and Bayes’ theorem p. 231

PROSPECTOR (1976), DENDRAL (1978)expert systems shells EMYCIN

Artificial Intelligence Chapter 83

Expert Systems

using domain knowledge knowledge representation p. 297reasoning with the knowledge, explanationKnowledge acquisition (p. 553)1) entering knowledge 2) maintaining knowledge base consistency3) ensuring knowledge base completenessMOLE (1988) is a knowledge acquisition system

for heuristic classification problems such as diagnosing diseases.

Artificial Intelligence Chapter 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1) Brittleness (เปราะบาง) : no general knowledge that can be used, the data is out of date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 Intelligence Chapter 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 Intelligence Chapter 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 Intelligence Chapter 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 Intelligence Chapter 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 Intelligence Chapter 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 Intelligence Chapter 810

The End