Coalition structure generation based on distributed constraint optimization

Suguru Ueda, Atsushi Iwasaki, Makoto Yokoo, Marius Calin Silaghi, Katsutoshi Hirayama, Toshihiro Matsui

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

40 Citations (Scopus)

Abstract

Forming effective coalitions is a major research challenge in AI and multi-agent systems (MAS). Coalition Structure Generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a coalition structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume that the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, one might imagine that the computational costs required in this approach would be too expensive, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve O (2 n) DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS with quality guarantees. More specifically, we develop an algorithm with parameter k that can find a CS whose social surplus is at least max(k/w* + 1), k/[n/2/) of the optimal CS, where w* is the tree width of a constraint graph. When k = 1, the complexity of this algorithm is about the same as solving just one DCOP. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing an efficient CSG algorithm with quality guarantees.

Original languageEnglish
Title of host publicationAAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
PublisherAI Access Foundation
Pages197-203
Number of pages7
ISBN (Print)9781577354642
Publication statusPublished - Jan 1 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
CountryUnited States
CityAtlanta, GA
Period7/11/107/15/10

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Ueda, S., Iwasaki, A., Yokoo, M., Calin Silaghi, M., Hirayama, K., & Matsui, T. (2010). Coalition structure generation based on distributed constraint optimization. In AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference (pp. 197-203). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.