Conflict-based negotiation strategy for human-agent negotiation

Name: Conflict-based negotiation strategy for human-agent negotiation
Author: Keskin, Mehmet Onur, Buzcu, Berk, Aydoğan, Reyhan

İsim	Conflict-based negotiation strategy for human-agent negotiation
Yazar	Keskin, Mehmet Onur, Buzcu, Berk, Aydoğan, Reyhan
Basım Tarihi:	2023-12
Basım Yeri	- Springer
Konu	Automated negotiation, Human-agent negotiation, Opponent modelling, Preference modelling
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0924-669X
Kayıt Numarası	ff2c9278-fc04-4c73-8d99-d68195a3a65e
Lokasyon	Computer Science
Tarih	2023-12
Notlar	CHIST-ERA ; TÜBİTAK
Örnek Metin	Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.
DOI	10.1007/s10489-023-05001-9
Cilt	53

Kaynağa git Özyeğin Üniversitesi

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Özyeğin Üniversitesi

Kaynağa git

Conflict-based negotiation strategy for human-agent negotiation

Yazar Keskin, Mehmet Onur, Buzcu, Berk, Aydoğan, Reyhan

Basım Tarihi 2023-12

Basım Yeri - Springer

Konu Automated negotiation, Human-agent negotiation, Opponent modelling, Preference modelling

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0924-669X

Kayıt Numarası ff2c9278-fc04-4c73-8d99-d68195a3a65e

Lokasyon Computer Science

Tarih 2023-12

Notlar CHIST-ERA ; TÜBİTAK

Örnek Metin Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.

DOI 10.1007/s10489-023-05001-9

Cilt 53