Coalition structure generation in multi-agent systems with positive and negative externalities

Talal Rahwan, Tomasz Michalak, Nicholas R. Jennings, Michael Wooldridge, Peter McBurney

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

    Abstract

    Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.

    Original languageEnglish (US)
    Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
    Pages257-263
    Number of pages7
    StatePublished - Dec 1 2009
    Event21st International Joint Conference on Artificial Intelligence, IJCAI-09 - Pasadena, CA, United States
    Duration: Jul 11 2009Jul 17 2009

    Other

    Other21st International Joint Conference on Artificial Intelligence, IJCAI-09
    CountryUnited States
    CityPasadena, CA
    Period7/11/097/17/09

    Fingerprint

    Multi agent systems

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Rahwan, T., Michalak, T., Jennings, N. R., Wooldridge, M., & McBurney, P. (2009). Coalition structure generation in multi-agent systems with positive and negative externalities. In IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence (pp. 257-263)

    Coalition structure generation in multi-agent systems with positive and negative externalities. / Rahwan, Talal; Michalak, Tomasz; Jennings, Nicholas R.; Wooldridge, Michael; McBurney, Peter.

    IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009. p. 257-263.

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

    Rahwan, T, Michalak, T, Jennings, NR, Wooldridge, M & McBurney, P 2009, Coalition structure generation in multi-agent systems with positive and negative externalities. in IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence. pp. 257-263, 21st International Joint Conference on Artificial Intelligence, IJCAI-09, Pasadena, CA, United States, 7/11/09.
    Rahwan T, Michalak T, Jennings NR, Wooldridge M, McBurney P. Coalition structure generation in multi-agent systems with positive and negative externalities. In IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009. p. 257-263
    Rahwan, Talal ; Michalak, Tomasz ; Jennings, Nicholas R. ; Wooldridge, Michael ; McBurney, Peter. / Coalition structure generation in multi-agent systems with positive and negative externalities. IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009. pp. 257-263
    @inproceedings{f749b91785844ee98f1dee31133d99ae,
    title = "Coalition structure generation in multi-agent systems with positive and negative externalities",
    abstract = "Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.",
    author = "Talal Rahwan and Tomasz Michalak and Jennings, {Nicholas R.} and Michael Wooldridge and Peter McBurney",
    year = "2009",
    month = "12",
    day = "1",
    language = "English (US)",
    isbn = "9781577354260",
    pages = "257--263",
    booktitle = "IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence",

    }

    TY - GEN

    T1 - Coalition structure generation in multi-agent systems with positive and negative externalities

    AU - Rahwan, Talal

    AU - Michalak, Tomasz

    AU - Jennings, Nicholas R.

    AU - Wooldridge, Michael

    AU - McBurney, Peter

    PY - 2009/12/1

    Y1 - 2009/12/1

    N2 - Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.

    AB - Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.

    UR - http://www.scopus.com/inward/record.url?scp=78149236028&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=78149236028&partnerID=8YFLogxK

    M3 - Conference contribution

    SN - 9781577354260

    SP - 257

    EP - 263

    BT - IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence

    ER -