A Bayesian classifier for learning opponents' preferences in multi-object automated negotiation

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1016/j.elerap.2006.06.008
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Name affiliation
  1. National Research Council of Canada. NRC Institute for Information Technology
FormatText, Article
Journal titleElectronic Commerce Research and Applications
ISSN1567-4223
Volume6
Issue3
Pages274–284
Subjectautomated negotiation; multi-issue; utility; preference elicitation; Bayesian classification
Abstract
We present a classification method for learning an opponent's preferences during a bilateral multi-issue negotiation. Similar candidate preference relations over the set of offers are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation lies in that class. Evidence used for classification decision-making is obtained by observing the opponent's sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent's true preferences.
Publication date
PublisherElsevier
Noteyes
LanguageEnglish
NRC numberNRCC 48500
NPARC number8914464
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