Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver
Abstract
In this paper, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the Continuous Distributed Constraint Optimization Problem (C-DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. D-Bay solves a C-DCOP by utilizing Bayesian optimization for the adaptive sampling of variables. We theoretically show that D-Bay converges to the global optimum of the C-DCOP for Lipschitz continuous utility functions. The performance of the algorithm is evaluated empirically based on the sample efficiency. The proposed algorithm is compared to state-of-the-art DCOP and C-DCOP solvers. The algorithm generates better solutions while requiring fewer samples.
Cite
Text
Fransman et al. "Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver." Journal of Artificial Intelligence Research, 2023. doi:10.1613/JAIR.1.14151Markdown
[Fransman et al. "Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver." Journal of Artificial Intelligence Research, 2023.](https://mlanthology.org/jair/2023/fransman2023jair-distributed/) doi:10.1613/JAIR.1.14151BibTeX
@article{fransman2023jair-distributed,
title = {{Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver}},
author = {Fransman, Jeroen and Sijs, Joris and Dol, Henry and Theunissen, Erik and De Schutter, Bart},
journal = {Journal of Artificial Intelligence Research},
year = {2023},
pages = {393-433},
doi = {10.1613/JAIR.1.14151},
volume = {76},
url = {https://mlanthology.org/jair/2023/fransman2023jair-distributed/}
}