An Efficient Algorithm for Solving Dynamic Complex DCOP Problems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Dynamic configuration of agent organizations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using distributed agents for patient scheduling
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Target to sensor allocation: A hierarchical dynamic Distributed Constraint Optimization approach
Computer Communications
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We define the distributed, continuous-time combinatorial optimization problem. We propose a new notion of solution stability in dynamic optimization, based on the cost of change from an already-implemented solution to the new one. Change costs are modeled with stability constraints, and can evolve over time. We present RSDPOP, a self-stabilizing optimization algorithm which guarantees optimal solution stability in dynamic environments, based on this definition. In contrast to current approaches which solve sequences of static CSPs, our mechanism has a lot more flexibility: each variable can be assigned and reassigned its own commitment deadlines at any point in time. Therefore, the optimization process is continuous, rather than a sequence of solving problem snapshots. We present experimental results from the distributed meeting scheduling domain.