Stochastic Local Search for Distributed Constraint Satisfaction Problems

Nowadays, many real problems can be solved using local search strategies. These algorithms incrementally alter inconsistency value assignments to all the variables using a repair or hill climbing metaphor to move towards more and more complete solutions. Furthermore, if the problem can be modeled as a distributed problem, the advantages can be even greater. This paper presents a distributed model for solving Constraint Satisfaction Problems (CSPs), in which agents are committed to sets of constraints. The problem constraints are ordered and partitioned, by a preprocessing step, so that the most restricted constraints are studied first. Thus, each agent solves a subproblem by means of a stochastic local search algorithm. This constraint ordering, as well as value and variable ordering, can improve efficiency because inconsistencies can be found earlier and the number of constraint checks can be significantly reduced.