Computing a payoff division in the least core for MC-nets coalitional games

Katsutoshi Hirayama, Kenta Hanada, Suguru Ueda, Makoto Yokoo, Atsushi Iwasaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

MC-nets is a concise representation of the characteristic functions that exploits a set of rules to compute payoffs. Given aMC-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.

Original languageEnglish
Title of host publicationPRIMA 2014
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 17th International Conference, Proceedings
EditorsHoa Khanh Dam, Jeremy Pitt, Yang Xu, Guido Governatori, Takayuki Ito
PublisherSpringer Verlag
Pages319-332
Number of pages14
ISBN (Electronic)9783319131900
DOIs
Publication statusPublished - 2014
Event17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 - Gold Coast, Australia
Duration: Dec 1 2014Dec 5 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8861
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014
CountryAustralia
CityGold Coast
Period12/1/1412/5/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hirayama, K., Hanada, K., Ueda, S., Yokoo, M., & Iwasaki, A. (2014). Computing a payoff division in the least core for MC-nets coalitional games. In H. K. Dam, J. Pitt, Y. Xu, G. Governatori, & T. Ito (Eds.), PRIMA 2014: Principles and Practice of Multi-Agent Systems - 17th International Conference, Proceedings (pp. 319-332). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8861). Springer Verlag. https://doi.org/10.1007/978-3-319-13191-7_26