Artificial Intelligence Through Prolog by Neil C. Rowe

By Neil C. Rowe

Man made Intelligence via Prolog e-book

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Rule and fact order Rules can go into a Prolog database just like facts. And the interpreter then uses both those rules and facts to answer queries. But we must worry a little about rule and fact order. To see why, consider combining several reasoning methods in the same rule, as for instance three ways of establishing the color of something: color_object(X,C) :- color(X,C); (part_of(X,Y), color(Y,C)); (part_of(X,Y), part_of(Y,Z), color(Z,C)). ) But that's poor rule-writing style, since it's hard to read the right side.

R(a,d). - r(a,Z). For this, Z can match b in the first fact. - r(b,d). There's no fact stating this either, so it must use the rule again recursively. - r(b,Z). This new Z is different from the previous Z, since each recursive call has its own variables (see Appendix B), in the same way that X's in different rules represent different X's. - r(c,d). That predicate expression is a fact. So the rule succeeds in proving r(b,d). And thus it succeeds in proving r(a,d), the original query. 6 are transitive: a_kind_of, part_of, right_of, during, and ancestor, for instance.

The speed of query answering depends on how many facts are indexed for each predicate in a query; the more facts, the slower the queries. Queries will also be slower when variables appear multiple times in the query and there is no argument indexing. This situation, called a join in database systems, requires embedded iterative loops, and loops can take a lot of time. With joins, possibilities literally multiply. - a_kind_of(enterprise,X), color(X,C). if there are 100 a_kind_of facts and 50 color facts, 50,000 combinations must be tried to find all possible X and C pairs, as when we type a semicolon repeatedly or when there are no such X and C.

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