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Probabilistic optimal solution assessment for DCOPs

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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.

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Acknowledgements

The authors acknowledge fruitful discussions with Yochai Twitto.

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Authors and Affiliations

  1. Department of Computer Science, Department of Mathematics, Ben-Gurion University of the Negev, Beersheba, Israel

    Daniel Berend

  2. Department of Computer Science, Ben-Gurion University of the Negev, Beersheba, Israel

    Amnon Meisels & Or Peri

Authors
  1. Daniel Berend
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  2. Amnon Meisels
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  3. Or Peri
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Correspondence to Daniel Berend.

Additional information

Supported by the Lynn and William Frankel center for Computer Sciences, the Paul Ivanier Center for Robotics and Production Management, and the Milken Families Foundation Chair in Mathematics.

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Berend, D., Meisels, A. & Peri, O. Probabilistic optimal solution assessment for DCOPs. Ann Math Artif Intell 83, 99–119 (2018). https://doi.org/10.1007/s10472-018-9582-1

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