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Efficient local search for very large-scale satisfiability problems
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On the run-time behaviour of stochastic local search algorithms for SAT
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Distributed Algorithms
Local search for distributed SAT with complex local problems
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A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
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The Evolution of Customer Middleware Requirements
PDIS '94 Proceedings of the Third International Conference on Parallel and Distributed Information Systems
Asynchronous Search with Aggregations
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Using CSP look-back techniques to solve real-world SAT instances
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Dynamic prioritization of complex agents in distributed constraint satisfaction problems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Distributed Lagrangean relaxation protocol for the generalized mutual assignment problem
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Multi-agent Based Method for Reconstructing Buckets in Encrypted Databases
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
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DisLRPɑ: ɑ-approximation in generalized mutual assignment
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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Solving DisCSPs with penalty driven search
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An α-approximation protocol for the generalized mutual assignment problem
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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We present a new series of distributed constraint satisfaction algorithms, the distributed breakout algorithms, which is inspired by local search algorithms for solving the constraint satisfaction problem (CSP). The basic idea of these algorithms is for agents to repeatedly improve their tentative and flawed sets of assignments for variables simultaneously while communicating such tentative sets with each other until finding a solution to an instance of the distributed constraint satisfaction problem (DisCSP). We introduce four implementations of the distributed breakout algorithms: SINGLE-DB, MULTI-DB, MULTI-DB+, and MULTI-DB++. SINGLE-DB is a distributed breakout algorithm for solving the DisCSP, where each agent has a single local variable and its related constraints. MULTI-DB, on the other hand, is another distributed breakout algorithm for solving the distributed SAT (DisSAT) problem, where each agent has multiple local variables and their related clauses. MULTI-DB+ and MULTI-DB++ are stochastic variations of MULTI-DB. In MULTI-DB+, we introduce a technique called random break into MULTI-DB; in MULTI-DB++, we introduce a technique called random walk into MULTI-DB+. We conducted experiments to compare these algorithms with the asynchronous type of distributed constraint satisfaction algorithm. Through these experiments, we found that SINGLE-DB, MULTI-DB, and MULTI-DB+ scale up better than the asynchronous type of distributed constraint satisfaction algorithms, but they sometimes show very poor performance. On the other hand, we also found that MULTI-DB++, which uses random walk, provides a clear performance improvement.