Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

Conference paper (2023)

Authors

Gevher Yesevi Özyeğin University
M.O. Keskin Özyeğin University, Interactive Intelligence - EEMCS
Anil Doğru Özyeğin University
Reyhan Aydoğan Interactive Intelligence - EEMCS , Özyeğin University
Research Group
Interactive Intelligence (EEMCS) (TU Delft)
Copyright: © 2023 Gevher Yesevi, M.O. Keskin, Anıl Doğru, Reyhan Aydoğan
DOI: https://doi.org/10.1007/978-3-031-21203-1_23
Automated negotiation Multi-agent systems Time-series prediction Utility prediction
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Interactive Intelligence

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.