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Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates. Problem solving is effected by deriving hypotheses on these abducible predicates (abductive hypotheses) as solutions of problems to be solved. These problems can be either observations that need to be explained (as in classical abduction) or goals to be achieved (as in normal logic programming). It can be used to solve problems in diagnosis, planning, natural language and machine learning. It has also been used to interpret negation as failure as a form of abductive reasoning. ==Syntax== Abductive logic programs have three components, where: * P is a logic program of exactly the same form as in logic programming * A is a set of predicate names, called the abducible predicates * IC is a set of first-order classical formulae. Normally, the logic program P does not contain any clauses whose head (or conclusion) refers to an abducible predicate. (This restriction can be made without loss of generality.) Also in practice, many times the integrity constraints in IC are often restricted to the form of denials, i.e. clauses of the form: false:- A1,...,An, not B1, ..., not Bm. Such a constraint means that it is not possible for all A1,...,An to be true and at the same time all of B1,...,Bm to be false. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「abductive logic programming」の詳細全文を読む スポンサード リンク
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