Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Weak-commitment search for solving constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Frozen development in graph coloring
Theoretical Computer Science - Phase transitions in combinatorial problems
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Distributed breakout revisited
Eighteenth national conference on Artificial intelligence
The Effect of Nogood Learning in Distributed Constraint Satisfaction
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
A mediation-based approach to cooperative, distributed problem solving
A mediation-based approach to cooperative, distributed problem solving
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Comparing two approaches to dynamic, distributed constraint satisfaction
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Examining DCSP coordination tradeoffs
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Using Prior Knowledge to Improve Distributed Hill Climbing
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Asynchronous Forward-checking for DisCSPs
Constraints
Solving DisCSPs with penalty driven search
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Completeness and performance of the APO algorithm
Journal of Artificial Intelligence Research
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A Cooperative mediation-based protocol for dynamic distributed resource allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Dynamic, partial centralization has received a considerable amount of attention in the distributed problem solving community. As the name implies, this technique works by dynamically identifying portions of a shared problem to centralize in order to speed the problem solving process. Currently, a number of algorithms have been created which employ this simple, yet powerful technique to solve problems such as distributed constraint satisfaction DCSP, distributed constraint optimization DCOP, and distributed resource allocation. In fact, one such algorithm, Asynchronous Partial Overlay APO, was shown to outperform the Asynchronous Weak Commitment AWC protocol, which is one of the best known methods for solving DCSPs. One of the key differences between these two algorithms is that APO, as part of the centralization process, uses explicit constraint passing. AWC, on the other hand, passed nogoods because it tries to provide security and privacy. Because of these differences in underlying assumptions, a number of researchers have criticized the comparison between these two protocols. This article attempts to resolve this disparity by introducing a new AWC/APO algorithm called Nogood-APO that like AWC uses nogood passing to provide privacy and like APO uses dynamic, partial centralization to speed the problem solving process. Like its parent algorithms, this new protocol is sound and complete and performs nearly as well as APO, while still outperforming AWC, on distributed 3-coloring problems. In addition, this paper shows that Nogood-APO provides more privacy to the agents than both APO and AWC on all but the sparsest problems. These findings demonstrate that a dynamic, partial centralization-based protocol can provide privacy and that even when operating with the same assumptions as AWC still solves problems in fewer cycles using less computation and communication.