Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver
J.E. Fransman (TU Delft - Student Development, TU Delft - Team Bart De Schutter)
Joris Sijs (TU Delft - Learning & Autonomous Control)
Henry Dol (TNO)
Erik Theunissen (Netherlands Defense Academy (NLDA))
B. De Schutter (TU Delft - Delft Center for Systems and Control)
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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.