An information-based neural approach to generic constraint satisfaction

Authors:
Henrik Jönsson;Bo Söderberg
Affiliations:
Complex Systems Division, Department of Theoretical Physics, Lund University, Sölvegatan 14A, S-223 62 Lund, Sweden;Complex Systems Division, Department of Theoretical Physics, Lund University, Sölvegatan 14A, S-223 62 Lund, Sweden
Venue:
Artificial Intelligence
Year:
2002

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Abstract

A novel artificial neural network heuristic (INN) for general constraint satisfaction problems is presented, extending a recently suggested method restricted to boolean variables. In contrast to conventional ANN methods, it employs a particular type of non-polynomial cost function, based on the information balance between variables and constraints in a mean-field setting. Implemented as an annealing algorithm, the method is numerically explored on a testbed of Graph Coloring problems. The performance is comparable to that of dedicated heuristics, and clearly superior to that of conventional mean-field annealing.