Paper Info
Title
Learning opponents' preferences in multi-object automated negotiation
Abstract
We 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.
Year
DOI
Venue
2005
10.1145/1089551.1089608
ICEC
Keywords
Field
DocType
classification decision-making,concession assumption,classification method,preference relation,bilateral multi-issue negotiation,true preference,similar candidate preference relation,multi-object automated negotiation,bayesian technique,correct class,true preference relation,utility,bayesian classification
Preference relation,Preference elicitation,Naive Bayes classifier,Computer science,Preference learning,Artificial intelligence,Machine learning,Negotiation,Bayesian probability
Conference
ISBN
Citations 
PageRank 
1-59593-112-0
15
0.85
References 
Authors
8
2
Name
Order
Citations
PageRank
Scott Buffett19313.68
Bruce Spencer212814.25