A scalable method for online learning of non-linear preferences based on anonymous negotiation data

Authors:
D. J. A. Somefun;J. A. La Poutré
Affiliations:
CWI, Amsterdam, The Netherlands;CWI and Technical Univ. Eindhoven, Eindhoven, The Netherlands
Venue:
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Year:
2006

Citing 2
Cited 0

Learning on opponent's preferences to make effective multi-issue negotiation trade-offs

ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

ICEC '04 Proceedings of the 6th international conference on Electronic commerce

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Abstract

We consider the problem of a shop agent negotiating bilaterally with many customers about a bundle of goods or services together with a price. To facilitate the shop agent's search for mutually beneficial alternative bundles, we develop a method for online learning customers' preferences, while respecting their privacy. By introducing extra parameters, we represent customers' highly nonlinear preferences as a linear model. We develop a method for learning the underlying stochastic process of these parameters online.