Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver

J.E. Fransman, J. Sijs, Henry Dol, Erik Theunissen, B.H.K. De Schutter

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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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.
Original languageEnglish
Article number14151
Pages (from-to)393-433
JournalJournal of Artificial Intelligence Research
Volume76
Issue number1165
DOIs
Publication statusPublished - 2023

Keywords

  • autonomous agents
  • multiagent systems

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