Resilient Logic Programs: Answer Set Programs Challenged By Ontologies

THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE(2020)

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摘要
We introduce resilient ic programs (RLPs) that couple a non -monotonic logic program and a first -order (F0) theory or description logic (DL) ontology. Unlike previous hybrid languages, where the interaction between the program and the theory is limited to consistency or query entailment tests, in RLPs answer sets must be 'resilient' to the models of the theory, allowing non -output predicates of the program to respond differently to different models. RLPs can elegantly express 3\7/]-0BFs, disjunctive ASP, and configuration problems tinder incompleteness of information. RLPs are decidable when a couple of natural assumptions are made: (0 satisliability of F0 theories in the presence of closed predicates is decidable, and (ii) rules are safe in the style of the well-known DL-safeness. We further show that a large fragment of such RLPs can be translated into standard (disjunctive) ASP, for which efficient implementations exist. For RLPs with theories expressed in DLs, we use a novel relaxation of safeness that safeguards rules via predicates whose extensions can be inferred to have a finite bound. We present several complexity results for the case where ontologies are written in some standard DLs.
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