Probabilistic optimal solution assessment for DCOPs

Daniel Berend, Amnon Meisels, Or Peri

Research output: Contribution to journalArticlepeer-review

Abstract

Distributed Constraint Optimization Problems (DCOPs) are widely used in Multi-Agent Systems for coordination and scheduling. The present paper proposes a heuristic algorithm that uses probabilistic assessment of the optimal solution in order to quickly find a solution that is not far from the optimal one. The heuristic assessment uses two passes by the agents to produce a high-quality solution. Extensive performance evaluation demonstrates that the solution of the proposed probabilistic assessment algorithm is indeed very close to the optimum, on smaller problems where this could be measured. In larger set-ups, the quality of the solution is demonstrated relatively to standard incomplete search algorithms.

Original languageEnglish
Pages (from-to)99-119
Number of pages21
JournalAnnals of Mathematics and Artificial Intelligence
Volume83
Issue number1
DOIs
StatePublished - 1 May 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Keywords

  • Constraints optimization
  • DCOP

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