Title
Bayesian-DPOP for continuous distributed constraint optimization problems
Author
Fransman, J.E.
Sijs, J.
Dol, H.S.
Theunissen, E.
de Schutter, B.
Publication year
2019
Abstract
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.
Subject
Bayesian optimization
DCOP
Distributed optimization
DPOP
Autonomous agents
Constrained optimization
Multi agent systems
Bayesian dynamic programming
Bayesian optimization
Multi-agent problems
Optimization procedures
Dynamic programming
To reference this document use:
http://resolver.tudelft.nl/uuid:b97320ee-4932-4303-9c71-954e8b035a98
TNO identifier
871941
Publisher
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
ISBN
9781510892002
ISSN
1548-8403
Source
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
Document type
conference paper