Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Dynamic Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
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
Secure distributed constraint satisfaction: reaching agreement without revealing private information
Artificial Intelligence - Special issue: Distributed constraint satisfaction
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This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation. The Distributed Constraint Satisfaction Problem (DCSP) is one that entails several agents to search for an agreement, which is a consistent combination of actions that satisfies their mutual constraints in a shared environment. By anchoring the DCSP search on automated negotiation, we show that several well-known DCSP algorithms are actually mechanisms that can reach agreements through a common Belief-Desire-Intention (BDI) protocol, but using different strategies. A major motivation for this BDI framework is that it not only provides a conceptually clearer understanding of existing DCSP algorithms from an agent model perspective, but also opens up the opportunities to extend and develop new strategies for DCSP. To this end, a new strategy called Unsolicited Mutual Advice (UMA) is proposed. Performance evaluation shows that the UMA strategy can outperform some existing mechanisms in terms of computational cycles.