A study of an approach to the collective iterative task allocation problem

Christian Guttmann, Iyad Rahwan, Michael Georgeff

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    Abstract

    A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. This paper extends previous work on an approach to the collective iterative task allocation problem where a group of agents endeavours to make the best allocations possible over multiple iterations of proposing, selection and learning. We offer an algorithm capturing the main aspects of this approach, and then show analytically and empirically that the agents estimations of the performance of a task and the type of group decision policy play an important role in the performance of the algorithm.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
    EditorsTsau Young Lin, Jeffrey M Bradshaw, Matthias Klusch, Chengqi Zhang, Andrei Broder, Howard Ho
    Place of PublicationLos Alamitos CA USA
    PublisherIEEE Computer Society
    Pages363 - 369
    Number of pages7
    Volume1
    ISBN (Print)0769530273
    Publication statusPublished - 2007
    EventIEEE/WIC/ACM International Conference on Intelligent Agent Technology - Silicon Valley USA, Los Alamitos CA USA
    Duration: 1 Jan 2007 → …

    Conference

    ConferenceIEEE/WIC/ACM International Conference on Intelligent Agent Technology
    CityLos Alamitos CA USA
    Period1/01/07 → …

    Cite this

    Guttmann, C., Rahwan, I., & Georgeff, M. (2007). A study of an approach to the collective iterative task allocation problem. In T. Y. Lin, J. M. Bradshaw, M. Klusch, C. Zhang, A. Broder, & H. Ho (Eds.), Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (Vol. 1, pp. 363 - 369). Los Alamitos CA USA: IEEE Computer Society.