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Lookahead-based SMT solving. (English) Zbl 1415.68193
Barthe, Gilles (ed.) et al., LPAR-22. 22nd international conference on logic for programming, artificial intelligence and reasoning, Awassa, Ethiopia, November 17–21, 2018. Selected papers. Manchester: EasyChair. EPiC Ser. Comput. 57, 418-434 (2018).
Summary: The lookahead approach for binary-tree-based search in constraint solving favors branching that provide the lowest upper bound for the remaining search space. The approach has recently been applied in instance partitioning in divide-and-conquer-based parallelization, but in general its connection to modern, clause-learning solvers is poorly understood. We show two ways of combining lookahead approach with a modern DPLL(\(T\))-based SMT solver fully profiting from theory propagation, clause learning, and restarts. Our thoroughly tested prototype implementation is surprisingly efficient as an independent SMT solver on certain instances, in particular when applied to a non-convex theory, where the lookahead-based implementation solves 40% more unsatisfiable instances compared to the standard implementation.
For the entire collection see [Zbl 1407.68021].
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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