Bayesian-DPOP for continuous distributed constraint optimization problems

conference paper
2019
Fransman, J.E.

Sijs, J.

Dol, H.S.

Theunissen, E.

Schutter, B. de
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.
Topics
Bayesian optimizationDCOPDistributed optimizationDPOPAutonomous agentsConstrained optimizationMulti agent systemsBayesian dynamic programmingBayesian optimizationMulti-agent problemsOptimization proceduresDynamic programming
TNO Identifier
871941
Repository link
https://resolver.tno.nl/uuid:b97320ee-4932-4303-9c71-954e8b035a98
ISSN
15488403
ISBN
9781510892002
Publisher
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Source title
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
Pages
1961-1963
Files
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