Two techniques to improve finite model search (2000) [1 citations — 0 self]
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Abstract:
Abstract. Finite model generation methods for rst order theories, when based on propositional logic, are very propagation eective, but need a lot of memory. A former work (FMSET [2]) attempted to improve the memory eciency of a propositional approach. This required complex data structures and algorithms. CSP based methods are more memory eective by nature but do not capture all the possible propagations. This article introduces two techniques to improve the propagation eciency of CSP based nite model generation methods. One approach consists in statically rewriting some selected clauses so as to trigger added constraint propagations. The other approach uses a dynamic lookahead strategy to both lter out inconsistent domain values and select the most appropriate branching variable according to a rst fail heuristic. Because of its eciency and source code availability, we chose to use the nite model generator SEM ([10]) as a software framework. We have obtained order of magnitude improvements on some problems, and successfully solved nite groups of order 30 and more.
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