Local search for distributed SAT with complex local problems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Protocol/Mechanism Design for Cooperation/Competition
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Asynchronous backtracking without adding links: a new member in the ABT family
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The distributed breakout algorithms
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The Effects of Agent Synchronization in Asynchronous Search Algorithms
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Journal of Artificial Intelligence Research
Asynchronous backtracking without adding links: a new member in the ABT family
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The distributed breakout algorithms
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
The effect of synchronization of agents' execution in randomly generated networks of constraints
ECC'11 Proceedings of the 5th European conference on European computing conference
Solving distributed CSPs using dynamic, partial centralization without explicit constraint passing
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constraint satisfaction algorithms and can efficiently make effective nogoods.We combine the method with the asynchronous weak-commitment search algorithm (AWC) and evaluate the performance of the resultant algorithm on distributed 3-coloring problems and distributed 3SAT problems. As a result, we found that the resolvent-based learning works well compared to previous learning methods for distributed constraint satisfaction algorithms. We also found that the AWC with the resolvent-based learning is able to find a solution with fewer cycles than the distributed breakout algorithm, which was known to be the most efficient algorithm (in terms of cycles) for solving distributed constraint satisfaction problems.