Optimizing Answer Set Computation via Heuristic-Based Decomposition
Answer Set Programming (ASP) is a purely declarative formalism, developed in the field of logic programming and nonmonotonic reasoning. In ASP, computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. In general, several programs semantically equivalent might be defined for the same problem; however, performance of ASP systems while evaluating them might significantly vary. In this work we propose a method for automatically transform an encoding into an equivalent one that can be evaluated more efficiently. The method makes use of hypertree decomposition techniques guided by proper heuristics. We embed the resulting technique into the ASP grounder I-DLV, and experimentally test it in order to asses performance improvements.
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