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.
|Name||Lecture Notes in Computer Science|
|Conference||5th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), May 8–12, 2006, Hakodate, Japan|
|Abbreviated title||AAMAS 2006|
|Period||8/05/06 → 12/05/06|
|Other||AAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan|