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- W3005347923 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." @default.
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- W3005347923 date "2023-01-23" @default.
- W3005347923 modified "2023-09-26" @default.
- W3005347923 title "Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver" @default.
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- W3005347923 doi "https://doi.org/10.1613/jair.1.14151" @default.
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