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.