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Collaborative agents for complex problems solving

Chapter


Abstract


  • Multi-Agent Systems (MAS) are particularly well suited to complex

    problem solving, whether the MAS comprises cooperative or

    competitive (self-interested) agents. In this context we discuss

    both dynamic team formation among the former, as well as partner

    selection strategies with the latter type of agent. One-shot,

    long-term, and (fuzzy-based) flexible formation strategies are

    compared and contrasted, and experiments described which compare

    these strategies along dimensions of Agent Search Time and Award

    Distribution Situation. We find that the flexible formation strategy

    is best suited to self-interested agents in open, dynamic

    environments. Agent negotiation among competitive agents is also

    discussed, in the context of collaborative problem solving. We

    present a modification to Zhang's Dual Concern Model which enables

    agents to make reasonable estimates of potential partner behavior

    during negotiation. Lastly, we introduce a Quadratic Regression

    approach to partner behavior analysis/estimation, which overcomes

    some of the limitations of Machine Learning-based approaches.

Publication Date


  • 2009

Citation


  • Zhang, M., Bai, Q., Ren, F. & Fulcher, J. (2009). Collaborative agents for complex problems solving. In C. L. Mumford & L. C. Jain (Eds.), Computational intelligence: collaboration, fusion and emergence (pp. 361-399). Berlin, Germany: Springer.

International Standard Book Number (isbn) 13


  • 9783642017988

Scopus Eid


  • 2-s2.0-84870498069

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3232

Book Title


  • Computational intelligence: collaboration, fusion and emergence

Has Global Citation Frequency


Start Page


  • 361

End Page


  • 399

Place Of Publication


  • Berlin, Germany

Abstract


  • Multi-Agent Systems (MAS) are particularly well suited to complex

    problem solving, whether the MAS comprises cooperative or

    competitive (self-interested) agents. In this context we discuss

    both dynamic team formation among the former, as well as partner

    selection strategies with the latter type of agent. One-shot,

    long-term, and (fuzzy-based) flexible formation strategies are

    compared and contrasted, and experiments described which compare

    these strategies along dimensions of Agent Search Time and Award

    Distribution Situation. We find that the flexible formation strategy

    is best suited to self-interested agents in open, dynamic

    environments. Agent negotiation among competitive agents is also

    discussed, in the context of collaborative problem solving. We

    present a modification to Zhang's Dual Concern Model which enables

    agents to make reasonable estimates of potential partner behavior

    during negotiation. Lastly, we introduce a Quadratic Regression

    approach to partner behavior analysis/estimation, which overcomes

    some of the limitations of Machine Learning-based approaches.

Publication Date


  • 2009

Citation


  • Zhang, M., Bai, Q., Ren, F. & Fulcher, J. (2009). Collaborative agents for complex problems solving. In C. L. Mumford & L. C. Jain (Eds.), Computational intelligence: collaboration, fusion and emergence (pp. 361-399). Berlin, Germany: Springer.

International Standard Book Number (isbn) 13


  • 9783642017988

Scopus Eid


  • 2-s2.0-84870498069

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3232

Book Title


  • Computational intelligence: collaboration, fusion and emergence

Has Global Citation Frequency


Start Page


  • 361

End Page


  • 399

Place Of Publication


  • Berlin, Germany