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:
Bibtex entry: