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.
Original language | English |
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Article number | 14151 |
Pages (from-to) | 393-433 |
Journal | Journal of Artificial Intelligence Research |
Volume | 76 |
Issue number | 1165 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- autonomous agents
- multiagent systems