Rethinking Frequency Opponent Modeling in Automated Negotiation

Okan Tunali, Reyhan Aydogan, Victor Sanchez-Anguix

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    11 Citations (Scopus)

    Abstract

    Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.
    Original languageEnglish
    Title of host publicationPRIMA 2017: Principles and Practice of Multi-Agent Systems
    PublisherSpringer
    Pages263-279
    Number of pages17
    Volume10621
    ISBN (Electronic)978-3-319-69131-2
    ISBN (Print)978-3-319-69130-5
    DOIs
    Publication statusPublished - 5 Oct 2017
    EventPRIMA 2017 - Nice, France
    Duration: 30 Oct 20173 Nov 2017
    Conference number: 20

    Publication series

    NameLecture Notes in Computer Science
    Volume10621
    ISSN (Print)0302-9743

    Conference

    ConferencePRIMA 2017
    Country/TerritoryFrance
    CityNice
    Period30/10/173/11/17

    Keywords

    • multi-agent systems
    • automated negotiation
    • opponent modelling

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