Abstract
Decoupling agents as we have done in Chap. 3 helps us to focus on the individual components of a negotiating agent’s design. One principal component of a negotiating agent’s strategy is its ability to take the opponent’s preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent’s preferences. Our main goal in this chapter is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.
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This chapter is based on the following publications: [19]
Tim Baarslag, Mark J.C. Hendrikx, Koen V. Hindriks, and Catholijn M. Jonker. Measuring the performance of online opponent models in automated bilateral negotiation. In Michael Thielscher and Dongmo Zhang, editors, AI 2012: Advances in Artificial Intelligence, volume 7691 of Lecture Notes in Computer Science, pages 1–14. Springer Berlin Heidelberg, 2012
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Baarslag, T. (2016). Measuring the Performance of Online Opponent Models. In: Exploring the Strategy Space of Negotiating Agents. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-28243-5_6
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DOI: https://doi.org/10.1007/978-3-319-28243-5_6
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