Finding the core for coalition structure utilizing dual solution

Atsushi Iwasaki, Suguru Ueda, Makoto Yokoo

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

6 Citations (Scopus)

Abstract

When forming the grand coalition is not possible/optimal, agents need to create a coalition structure. The idea of the core can be extended to such a case. In this paper, we propose an innovative algorithm called CoreD to check core-non-emptiness for coalition structures. A more straightforward algorithm based on existing techniques, which we call CoreP, first obtains the value of optimal coalition structure by solving an integer programming problem. Then, it checks whether that value can be divided without making a blocking (dissatisfied) coalition. In contrast, CoreD first finds a minimal amount value of optimal coalition structure so that there exists no blocking coalition. Then, it checks whether the optimal value can be equal to the minimal value. We empirically show that when the core is empty, CoreD is by far superior to CoreP. Also, to find a second-best payoff vector when the core is empty, we propose a new solution concept called the weak ε-core+, which can utilize the approximate value of the optimal coalition structure. Based on the idea of CoreD, we further develop an algorithm for checking the non-emptiness of the weak ε-core+.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
PublisherIEEE Computer Society
Pages114-121
Number of pages8
Volume2
ISBN (Print)9781479929023
DOIs
Publication statusPublished - 2013
Event2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013 - Atlanta, GA, United States
Duration: Nov 17 2013Nov 20 2013

Other

Other2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period11/17/1311/20/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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