A Bayesian Classifier for Learning Opponents' Preferences in Multi-Object Automated Negotiation

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TypeArticle
ConferenceElectronic Commerce Research and Applications Journal, 2007
Volume3, Number 3
Subjectautomated negotiation; multi-issue; utility; preference elicitation; Bayesian classification.
AbstractWe 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
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48500
NPARC number8914464
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Record identifierd3d7dbc8-1816-4bd5-8913-760c0ec7e181
Record created2009-04-22
Record modified2016-05-09
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