Abductive Logic Programming
Abductive Logic Programming is an extension of normal Logic Programming that allows some predicates, declared as abducible predicates, to be incompletely defined. Problem solving is achieved by deriving hypotheses expressed in terms of the abducible predicates as solutions of problems to be solved. These problems can be either observations that need to be explained (as in classical abductive reasoning) or goals to be achieved (as in normal logic programming). It has been 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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