Learning Opponents' Preferences in Multi-Object Automated Negotiation

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ConferenceSeventh International Conference on Electronic Commerce (ICEC'05), August 15-17, 2005., Xi'an, China
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 are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation over the set of offers lies in that class. Evidence used for classification decision-making is obtained by observing the opponents' 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
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48260
NPARC number9167868
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Record identifierc17eadb6-48f7-4b12-a20a-915a7b066a8e
Record created2009-06-29
Record modified2016-05-09
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