Bayesian-DPOP for continuous distributed constraint optimization problems

Jeroen Fransman, Joris Sijs, Henry Dol, Erik Theunissen, Bart De Schutter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019)
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1961-1963
Volume4
ISBN (Electronic)978-1-4503-6309-9
ISBN (Print)978-1-5108-9200-2
Publication statusPublished - 2019
EventAAMAS 2019: The 18th International Conference on Autonomous Agents and MultiAgent Systems - Montreal, Canada
Duration: 13 May 201917 May 2019

Conference

ConferenceAAMAS 2019
CountryCanada
CityMontreal
Period13/05/1917/05/19

Keywords

  • Bayesian optimization
  • DCOP
  • Distributed optimization
  • DPOP

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