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

Authors:
Scott Buffett;Bruce Spencer
Affiliations:
Institute for Information Technology - e-Business, National Research Council, 46 Dineen Drive, Fredericton, NB, Canada E3B 9W4 and University of New Brunswick, Fredericton, NB, Canada E3B 5A3;Institute for Information Technology - e-Business, National Research Council, 46 Dineen Drive, Fredericton, NB, Canada E3B 9W4 and University of New Brunswick, Fredericton, NB, Canada E3B 5A3
Venue:
Electronic Commerce Research and Applications
Year:
2007

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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.