Abstract
In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.
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Notes
- 1.
In our work, we consider five previous consecutive offer exchanges.
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Yesevi, G., Keskin, M.O., Doğru, A., Aydoğan, R. (2023). Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_23
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