Bayesian-DPOP for Continuous Distributed Constraint Optimization
Problems
Reference
J. Fransman,
J. Sijs,
H. Dol,
E. Theunissen, and
B. De Schutter,
"Bayesian-DPOP for Continuous Distributed Constraint Optimization
Problems," Proceedings of the 18th International
Conference on Autonomous Agents and MultiAgent Systems
(AAMAS'19), Montreal, Canada, pp. 1961-1963, May 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.
Downloads
- Corresponding technical report:
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Bibtex entry
@inproceedings{FraSij:19-020,
author={J. Fransman and J. Sijs and H. Dol and E. Theunissen and B. {D}e
Schutter},
title={Bayesian-{DPOP} for Continuous Distributed Constraint Optimization
Problems},
booktitle={Proceedings of the 18th International Conference on Autonomous Agents
and MultiAgent Systems (AAMAS'19)},
address={Montreal, Canada},
pages={1961--1963},
month=may,
year={2019}
}
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Last update: February 21, 2026.