Efficient generic search heuristics within the EMBP framework

Authors:
Ronan Le Bras;Alessandro Zanarini;Gilles Pesant
Affiliations:
École Polytechnique de Montréal, Montreal, Canada and CIRRELT, Université de Montréal, Montreal, Canada;École Polytechnique de Montréal, Montreal, Canada and CIRRELT, Université de Montréal, Montreal, Canada;École Polytechnique de Montréal, Montreal, Canada and CIRRELT, Université de Montréal, Montreal, Canada
Venue:
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Year:
2009

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Abstract

Accurately estimating the distribution of solutions to a problem, should such solutions exist, provides efficient search heuristics. The purpose of this paper is to propose new ways of computing such estimates, with different degrees of accuracy and complexity.We build on the Expectation-Maximization Belief-Propagation (EMPB) framework proposed by Hsu et al. to solve Constraint Satisfaction Problems (CSPs). We propose two general approaches within the EMBP framework: we firstly derive update rules at the constraint level while enforcing domain consistency and then derive update rules globally, at the problem level. The contribution of this paper is two-fold: first, we derive new generic update rules suited to tackle any CSP; second, we propose an efficient EMBP-inspired approach, thereby improving this method and making it competitive with the state of the art. We evaluate these approaches experimentally and demonstrate their effectiveness.