A Hybrid Approach to Distributed Constraint Satisfaction

Abstract

Abstract. We present a hybrid approach to Distributed Constraint Satisfaction which combines incomplete, fast, penalty-based local search with complete, slower systematic search. Thus, we propose the hybrid algorithm PenDHyb where the distributed local search algorithm DisPeL is run for a very small amount of time in order to learn about the difficult areas of the problem from the penalty counts imposed during its problem-solving. This knowledge is then used to guide the systematic search algorithm SynCBJ. Extensive empirical results in several problem classes indicate that PenDHyb is effective for large problems.

Multi-HDCS: Solving DisCSPs with Complex Local Problems Cooperatively

“…Unlike Multi-DisPeL, InterDisPeL: (i) considers only inter-agent constraints; (ii) only chooses variable-value combinations approved by SEBJ; (iii) maintains, for each agent, an overall count of the penalties it has imposed in the spirit of [10]. Thus, whenever a penalty is imposed on an agent's variable, the agent's penalty count is increased.…”

“…InterPODS is inspired by the much simpler PenDHyb algorithm [10] with substantial differences: (i) each InterPODS agent knows only those value combinations which are compatible with the local problem's intra-agent constraints;…”

Multi-Hyb: A Hybrid Algorithm for Solving DisCSPs with Complex Local Problems

“…Hence, SynCBJ-CLP only considers the inter-agent constraints (for example OOP = M anag) and ignores the intra-agent constraints, since these have already been checked by SEBJ. Knowledge sharing inspired by PenDHyb [6] exists so that SynCBJ-CLP uses the following knowledge learnt by DisPeL-1C: (i) difficult areas of the problem and; (ii) best 'solution' found 1. In the remainder of this paper, we refer to this latter version as DisPeL.…”

“…PenDHyb [6]), to the best of our knowledge, there are no hybrid approaches specifically designed for solving DisCSPs with complex local problems.…”