Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Solving Coarse-grained DisCSPs with Multi-DisPeL and DisBO-wd
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A hybrid approach to solving coarse-grained DisCSPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Solving DisCSPs with penalty driven search
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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A coarse-grained Distributed Constraint Satisfaction Problem (DisCSP) is a constraint problem where several agents, each responsible for solving one part (a complex local problem), cooperate to determine an overall solution. Thus, agents solve the overall problem by finding a solution to their complex local problem which is compatible with the solutions proposed by other agents for their own local problems. Several approaches to solving DisCSPs have been devised and can be classified as systematic search and local search techniques. We present Multi-Hyb, a two-phase hybrid algorithm for solving coarse-grained DisCSPs which uses both systematic and local search during problem solving. Phase 1 generates key partial solutions to the global problem using systematic search. Concurrently, a penalty-based local search algorithm attempts to find a global solution to the problem using these partial solutions. If a global solution is not found in phase 1, the information learnt from phase 1 is used to inform the search carried out during the next phase. Phase two runs a systematic search algorithm on complex variables guided by the following knowledge obtained in phase 1: (i) partial solutions and; (ii) complex local problems which appear more difficult to satisfy. Experimental evaluation demonstrates that Multi-Hyb is competitive in several problem classes in terms of: (i) the communication cost and (ii) the computational effort needed.