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Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph

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PRIMA 2018: Principles and Practice of Multi-Agent Systems (PRIMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11224))

Abstract

We introduce the Constraint Composite Graph (CCG) for Distributed Constraint Optimization Problems (DCOPs), a popular paradigm used for the description and resolution of cooperative multi-agent problems. The CCG is a novel graphical representation of DCOPs on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum—a popular DCOP incomplete algorithm—called CCG-Max-Sum, which is applied to CCGs, and demonstrate its efficiency and effectiveness on DCOP benchmarks based on several network topologies.

The research at the University of Southern California was supported by the National Science Foundation (NSF) under grant numbers 1724392, 1409987, and 1817189. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies or the U.S. government.

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Notes

  1. 1.

    Its runtime is comparable to that of one iteration of CCG-Max-Sum, which in turn takes 0.035 s on average in our experiments.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Ferdinando Fioretto

  2. University of Southern California, Los Angeles, CA, USA

    Hong Xu, Sven Koenig & T. K. Satish Kumar

Authors
  1. Ferdinando Fioretto
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  2. Hong Xu
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  3. Sven Koenig
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  4. T. K. Satish Kumar
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Corresponding author

Correspondence to Ferdinando Fioretto .

Editor information

Editors and Affiliations

  1. School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Tim Miller

  2. Department of Computer Science, University of Aberdeen, Aberdeen, UK

    Nir Oren

  3. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Yuko Sakurai

  4. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan

    Itsuki Noda

  5. University of Otago, Dunedin, New Zealand

    Bastin Tony Roy Savarimuthu

  6. Computer Science Department, New Mexico State University, Las Cruces, USA

    Tran Cao Son

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Fioretto, F., Xu, H., Koenig, S., Kumar, T.K.S. (2018). Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-03098-8_7

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  • Print ISBN: 978-3-030-03097-1

  • Online ISBN: 978-3-030-03098-8

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