Lifted Symmetry Detection and Breaking for MAP Inference

NeurIPS 2015 Timothy KoppParag SinglaHenry Kautz

Symmetry breaking is a technique for speeding up propositional satisfiability testing by adding constraints to the theory that restrict the search space while preserving satisfiability. In this work, we extend symmetry breaking to the problem of model finding in weighted and unweighted relational theories, a class of problems that includes MAP inference in Markov Logic and similar statistical-relational languages... (read more)

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