A fast method for learning non-linear preferences online using anonymous negotiation data

Authors:
D. J. A. Somefun;J. A. La Poutré
Affiliations:
Center for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands;Center for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands
Venue:
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Year:
2006

Citing 5
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Learning opponent's preferences for effective negotiation: an approach based on concept learning

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Abstract

In this paper, 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 additional 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. As the conducted computer experiments show, the developed method has a number of advantages: it scales well, the acquired knowledge is robust towards changes in the shop's pricing strategy, and it performs well even if customers behave strategically.