Learning user preferences for multi-attribute negotiation: an evolutionary approach

Authors:
Yutao Guo;Jörg P. Müller;Christof Weinhardt
Affiliations:
Intelligent Autonomous Systems, Siemens AG, Corporate Technology, Munich, Germany;Intelligent Autonomous Systems, Siemens AG, Corporate Technology, Munich, Germany;Department of Economics and Business Engineering, University of Karlsruhe, Karlsruhe, Germany
Venue:
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Year:
2003

Citing 8
Cited 5

Rules of encounter: designing conventions for automated negotiation among computers

Rules of encounter: designing conventions for automated negotiation among computers
A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms

Artificial Intelligence Review - Special issue on lazy learning
The Michigan Internet AuctionBot: a configurable auction server for human and software agents

AGENTS '98 Proceedings of the second international conference on Autonomous agents
eMediator: a next generation electronic commerce server

AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Design and implementation of an agent-based intermediary infrastructure for electronic markets

Proceedings of the 2nd ACM conference on Electronic commerce
The Design of Intelligent Agents: A Layered Approach

The Design of Intelligent Agents: A Layered Approach
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications

SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver

IEEE Transactions on Computers

Knowledge-based acquisition of tradeoff preferences for negotiating agents

ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Acquiring user tradeoff strategies and preferences for negotiating agents: A default-then-adjust method

International Journal of Human-Computer Studies
Agent-based assistant for e-negotiations

ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Applying hybrid case-based reasoning in agent-based negotiations for supply chain management

Expert Systems with Applications: An International Journal
An overview of cooperative and competitive multiagent learning

LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper investigates how agents that act on behalf of users in electronic negotiations can elicit the required information about their users' preference structures. Based on a multi-attribute utility theoretic model of user preferences, we propose an algorithm that enables an agent to learn the utility function over time, taking knowledge gathered about the user into account. The method combines an evolutionary learning with the application of external knowledge and local search. The algorithm learns a complete multi-attribute utility function, consisting of the attribute weights and the individual attribute utility functions. Empirical tests show that the algorithm provides a good learning performance.