Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Reducing search space in local search for constraint satisfaction
Eighteenth national conference on Artificial intelligence
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Local search methods for solving constraint satisfaction problems such as GSAT, WalkSAT and DLM starts the search for a solution from a random assignment. Local search then examines the neighbours of this assignment, using the penalty function to determine a better neighbour valuations to move to. It repeats this process until it finds a solution which satisfies all constraints. ICM [8] considers some of the constraints as hard constraints that are always satisfied. In this way, the constraints reduce the possible neighbours in each move and hence the overall search space. We choose the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach a solution from any valuation in this space. We show in this paper how incorporating learning in the island traps and restart improves the DLMI algorithm [8].