Abductive logic programming: Difference between revisions

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'''Abductive logic programming''' ('''ALP''') is a high-level [[knowledge representation|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.

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==Syntax==

Abductive logic programs have three components, <math>\langle P,A,IC\rangle,</math> where: