Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 383)

Abstract

In many situations, a form of negotiation can be used to resolve a problem between multiple parties. However, one of the biggest problems is not knowing the intentions and true interests of the opponent. Such a user profile can be learned or estimated using biddings as evidence that reveal some of the underlying interests. In this paper we present a model for online learning of an opponent model in a closed bilateral negotiation session. We studied the obtained utility during several negotiation sessions. Results show a significant improvement in utility when the agent negotiates against a state-of-the-art Bayesian agent, but also that results are very domain-dependent.

Keywords

Multiagent System Autonomous Agent Average Utility Bilateral Negotiation Opposing Party 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Man-Machine Interaction, Faculty of EEMCSDelft University of TechnologyDelftThe Netherlands

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