Time series predictive models for opponent behavior modeling in bilateral negotiations

Name: Time series predictive models for opponent behavior modeling in bilateral negotiations
Author: Yesevi, Gevher, Keskin, Mehmet Onur, Doğru, Anıl, Aydoğan, Reyhan

İsim	Time series predictive models for opponent behavior modeling in bilateral negotiations
Yazar	Yesevi, Gevher, Keskin, Mehmet Onur, Doğru, Anıl, Aydoğan, Reyhan
Basım Tarihi:	2023
Basım Yeri	- Springer
Konu	Automated negotiation, Multi-agent systems, Time-series prediction, Utility prediction
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0302-9743
Kayıt Numarası	6bb8300f-94e6-49a2-8071-2d3bc24c74f5
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	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.
DOI	10.1007/978-3-031-21203-1_23
Cilt	13753

Kaynağa git Özyeğin Üniversitesi

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Özyeğin Üniversitesi

Kaynağa git

Time series predictive models for opponent behavior modeling in bilateral negotiations

Yazar Yesevi, Gevher, Keskin, Mehmet Onur, Doğru, Anıl, Aydoğan, Reyhan

Basım Tarihi 2023

Basım Yeri - Springer

Konu Automated negotiation, Multi-agent systems, Time-series prediction, Utility prediction

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0302-9743

Kayıt Numarası 6bb8300f-94e6-49a2-8071-2d3bc24c74f5

Lokasyon Computer Science

Tarih 2023

Örnek Metin 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.

DOI 10.1007/978-3-031-21203-1_23

Cilt 13753