Bayesian-DPOP for continuous distributed constraint optimization problems
de Schutter, B.
In this work, the novel algorithm Bayesian Dynamic Programming Optimization Procedure (B-DPOP) is presented to solve multi-agent problems within the Distributed Constraint Optimization Problem framework. The Bayesian optimization framework is used to prove convergence to the global optimum of the B-DPOP algorithm for Lipschitz-continuous objective functions. The proposed algorithm is assessed based on the benchmark problem known as dynamic sensor placement. Results show increased performance over related algorithms in terms of sample-efficiency. © 2019 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
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Multi agent systems
Bayesian dynamic programming
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019, 13 May 2019 through 17 May 2019, 1961-1963