Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning (extended abstract)

Title

Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning (extended abstract)

 Author

Hindriks, K.V.
Tykhonov, D.

Faculty

Electrical Engineering, Mathematics and Computer Science

 Department

Intelligent Systems

 Date

2008-10-30

 Abstract

In this paper, we show that it is nonetheless possible to construct an opponent model, i.e. a model of the opponent’s preferences that can be effectively used to improve negotiation outcomes. We provide a generic framework for learning both the preferences associated with issue values as well as the weights that rank the importance of issues to an agent. The main idea is to exploit certain structural features and rationality principles to guide the learning process and focuses the algorithm on the most likely preference profiles of an opponent. We present a learning algorithm based on Bayesian learning techniques that computes the probability that an opponent has a particular preference profile. Our approach can be integrated into various negotiating agents using different strategies.

 To reference this document use:

http://resolver.tudelft.nl/uuid:4c5abf89-e8f8-4cde-a03d-84aec02036f9

 Source

BNAIC 2008: The 20th Belgian-Netherlands Conference on Artificial Intelligence, Enschede, The Netherlands, 30-31 October 2008

 Part of collection

Institutional Repository

 Document type

conference paper

 Rights

(c) 2008 The Author(s)
Files
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