Abstract
The quality of an opponent model can be measured in two different ways. One, which we extensively covered in Chap. 6, 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. Our work in this chapter bridges the gap between the two approaches by investigating a large set of opponent modeling techniques in different negotiation settings, measuring both their accuracy through time and their performance. We review all ways to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads us to new insights in how to construct an opponent model, and what we need to measure when optimizing performance.
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This chapter is based on the following publications: [13]
Tim Baarslag, Mark J.C. Hendrikx, Koen V. Hindriks, and Catholijn M. Jonker. Predicting the performance of opponent models in automated negotiation. In International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM, volume 2, pages 59–66, Nov 2013
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Baarslag, T. (2016). Predicting the Performance of Opponent Models. In: Exploring the Strategy Space of Negotiating Agents. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-28243-5_7
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DOI: https://doi.org/10.1007/978-3-319-28243-5_7
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