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Deep reinforcement learning for acceptance strategy in bilateral negotiations

Title	Deep reinforcement learning for acceptance strategy in bilateral negotiations
Author	Razeghi, Yousef, Yavuz, Ozan, Aydoğan, Reyhan
Publication Date:	2020
Publication Place	- TÜBİTAK
Subject	Deep reinforcement learning, Automated bilateral negotiation, Acceptance strategy
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1300-0632
Record ID	61a17abd-4aab-464b-ac13-50e33bde688a
Library Location	Computer Science
Date	2020
Sample Text	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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Özyeğin University

Deep reinforcement learning for acceptance strategy in bilateral negotiations

Author Razeghi, Yousef, Yavuz, Ozan, Aydoğan, Reyhan

Publication Date 2020

Publication Place - TÜBİTAK

Subject Deep reinforcement learning, Automated bilateral negotiation, Acceptance strategy

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1300-0632

Record ID 61a17abd-4aab-464b-ac13-50e33bde688a

Library Location Computer Science

Date 2020

Sample Text 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