Deep reinforcement learning for acceptance strategy in bilateral negotiations

Name: Deep reinforcement learning for acceptance strategy in bilateral negotiations
Author: Razeghi, Yousef, Yavuz, Ozan, Aydoğan, Reyhan

İsim	Deep reinforcement learning for acceptance strategy in bilateral negotiations
Yazar	Razeghi, Yousef, Yavuz, Ozan, Aydoğan, Reyhan
Basım Tarihi:	2020
Basım Yeri	- TÜBİTAK
Konu	Deep reinforcement learning, Automated bilateral negotiation, Acceptance strategy
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1300-0632
Kayıt Numarası	61a17abd-4aab-464b-ac13-50e33bde688a
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.
DOI	10.3906/elk-1907-215
Cilt	28

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Deep reinforcement learning for acceptance strategy in bilateral negotiations

Yazar Razeghi, Yousef, Yavuz, Ozan, Aydoğan, Reyhan

Basım Tarihi 2020

Basım Yeri - TÜBİTAK

Konu Deep reinforcement learning, Automated bilateral negotiation, Acceptance strategy

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1300-0632

Kayıt Numarası 61a17abd-4aab-464b-ac13-50e33bde688a

Lokasyon Computer Science

Tarih 2020

Örnek Metin This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.

DOI 10.3906/elk-1907-215

Cilt 28