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Author Baarslag, T. ♦ Hendrikx, M. ♦ Hindriks, K. ♦ Jonker, C.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Analytical models ♦ Accuracy ♦ Correlation ♦ Multiagent systems ♦ Current measurement ♦ Estimation ♦ Machine learning ♦ Bayes methods ♦ Intelligent agents
Abstract When two agents settle a mutual concern by negotiating with each other, they usually do not share their preferences so as to avoid exploitation. In such a setting, the agents may need to analyze each other's behavior to make an estimation of the opponent's preferences. This process of opponent modeling makes it possible to find a satisfying negotiation outcome for both parties. A large number of such opponent modeling techniques have already been introduced, together with different measures to assess their quality. The quality of an opponent model can be measured in two different ways: one is to use the agent's performance as a benchmark for the model's quality, the other is to directly evaluate its accuracy by using similarity measures. Both methods have been used extensively, and both have their distinct advantages and drawbacks. In this work we investigate the exact relation between the two, and we pinpoint the measures for accuracy that best predict performance gain. This leads us to new insights in how to construct an opponent model, and what we need to measure when optimizing performance.
Description Author affiliation: Interactive Intell. Group, Delft Univ. of Technol., Delft, Netherlands (Baarslag, T.; Hendrikx, M.; Hindriks, K.; Jonker, C.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-11-17
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9780769551456
Size (in Bytes) 495.46 kB
Page Count 8
Starting Page 59
Ending Page 66


Source: IEEE Xplore Digital Library