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: pdf file (793 KB)

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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