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Computing a Payoff Division in the Least Core for MC-nets Coalitional Games

  • Katsutoshi Hirayama
  • Kenta Hanada
  • Suguru Ueda
  • Makoto Yokoo
  • Atsushi Iwasaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8861)

Abstract

MC-nets is a concise representation of the characteristic functions that exploits a set of rules to compute payoffs. Given a MC-nets instance, the problem of computing a payoff division in the least core, which is a generalization of the core-non-emptiness problem that is known to be coNP-complete, is definitely a hard computational problem. In fact, to the best of our knowledge, no algorithm can actually compute such a payoff division for MC-nets instances with dozens of agents. We propose a new algorithm for this problem, that exploits the constraint generation technique to solve the linear programming problem that potentially has a huge number of constraints. Our experimental results are striking since, using 8 GB memory, our proposed algorithm can successfully compute a payoff division in the least core for the instances with up to 100 agents, but the naive algorithm fails due to a lack of memory for instances with 30 or more agents.

Keywords

coalitional games least core MC-nets constraint generation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Katsutoshi Hirayama
    • 1
  • Kenta Hanada
    • 1
  • Suguru Ueda
    • 2
  • Makoto Yokoo
    • 3
  • Atsushi Iwasaki
    • 4
  1. 1.Kobe UniversityJapan
  2. 2.National Institute of InformaticsJapan
  3. 3.Kyushu UniversityJapan
  4. 4.University of Electro-CommnunicationsJapan

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