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

D.J.A. Somefun, J.A. Poutré, la

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationAutonomous agents and multiagent systems : Proceedings of the fifth international joint conference
PublisherAssociation for Computing Machinery, Inc
Pages417-419
ISBN (Print)1-59593-303-4
Publication statusPublished - 2006
Event5th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), May 8–12, 2006, Hakodate, Japan - Hakodate, Japan
Duration: 8 May 200612 May 2006

Conference

Conference5th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), May 8–12, 2006, Hakodate, Japan
Abbreviated titleAAMAS 2006
CountryJapan
CityHakodate
Period8/05/0612/05/06

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