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Content available remote lpopt : A Rule Optimization Tool for Answer Set Programming
EN
State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant hand-tuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.
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Content available remote Expansion-based QBF Solving on Tree Decompositions
EN
In recent years various approaches for quantified Boolean formula (QBF) solving have been developed, including methods based on expansion, skolemization and search. Here, we present a novel expansion-based solving technique that is motivated by concepts from the area of parameterized complexity. Our approach relies on dynamic programming over the tree decomposition of QBFs in prenex conjunctive normal form (PCNF). Hereby, binary decision diagrams (BDDs) are used for compactly storing partial solutions. Towards efficiency in practice, we integrate dependency schemes and develop dedicated heuristic strategies. Our experimental evaluation reveals that our implementation is competitive to state-of-the-art solvers on instances with one quantifier alternation. Furthermore, it performs particularly well on instances up to a treewidth of approximately 80, even for more quantifier alternations. Results indicate that our approach is orthogonal to existing techniques, with a large number of uniquely solved instances.
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