A distributed constraint optimization approach for coordination under uncertainty

Distributed Constraint Optimization (DCOP) provides a rich framework for modeling multi-agent coordination problems. Existing problem domains for DCOP focus on small (<100 variables), deterministic domains. We present a mapping to DCOP for large-scale team coordination problems that were used in the DARPA Coordinators program. This domain requires distributed, scalable algorithms to meet difficult bounds on computation and communication time. To achieve this goal, we develop a new DCOP algorithm that scales to problems involving hundreds of variables and constraints while converging to better solution qualities than existing DCOP algorithms. We show that our algorithm outperforms other DCOP algorithms for this domain and that our approach is competitive with other general approaches used in the DARPA Coordinators program. CR Categories: I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Multiagent Systems;