Branch-and-bound heuristics for incomplete DCOPs

Atena M. Tabakhi, Yuanming Xiao, William Yeoh, Roie Zivan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Incomplete Distributed Constraint Optimization Problem (IDCOP) extends the distributed constraint optimization problem, where constraint costs are allowed to be unspecified. A distributed variant of the Synchronous Branch-and-Bound (SyncBB) search algorithm has been proposed to solve I-DCOPs, where unspecified constraint costs are elicited during its execution. In this paper, we propose two heuristics that can be used in conjunction with SyncBB to solve I-DCOPs. Our proposed heuristics speed up the algorithm by pruning those parts of the search space whose solution quality is sub-optimal. Thus, our model and heuristics extend the state of the art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences.

Original languageEnglish
Title of host publication20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1665-1667
Number of pages3
ISBN (Electronic)9781713832621
StatePublished - 1 Jan 2021
Event20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
CityVirtual, Online
Period3/05/217/05/21

Keywords

  • DCOPs
  • Heuristics
  • Multi-Agent Problems
  • Preference Elicitation

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