@inproceedings{konishi-etal-2026-preference,
title = "Preference Estimation via Opponent Modeling in Multi-Agent Negotiation",
author = "Konishi, Yuta and
Yamamoto, Kento and
Sonomoto, Eisuke and
Takeda, Rikuho and
Furukawa, Ryo and
Muraki, Yusuke and
Shimizu, Takafumi and
Fukumura, Kazuma and
Kanemoto, Yuya and
Ito, Takayuki and
Ding, Shiyao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1398/",
doi = "10.18653/v1/2026.findings-acl.1398",
pages = "28049--28058",
ISBN = "979-8-89176-395-1",
abstract = "Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding."
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<abstract>Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.</abstract>
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%0 Conference Proceedings
%T Preference Estimation via Opponent Modeling in Multi-Agent Negotiation
%A Konishi, Yuta
%A Yamamoto, Kento
%A Sonomoto, Eisuke
%A Takeda, Rikuho
%A Furukawa, Ryo
%A Muraki, Yusuke
%A Shimizu, Takafumi
%A Fukumura, Kazuma
%A Kanemoto, Yuya
%A Ito, Takayuki
%A Ding, Shiyao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F konishi-etal-2026-preference
%X Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.
%R 10.18653/v1/2026.findings-acl.1398
%U https://aclanthology.org/2026.findings-acl.1398/
%U https://doi.org/10.18653/v1/2026.findings-acl.1398
%P 28049-28058
Markdown (Informal)
[Preference Estimation via Opponent Modeling in Multi-Agent Negotiation](https://aclanthology.org/2026.findings-acl.1398/) (Konishi et al., Findings 2026)
ACL
- Yuta Konishi, Kento Yamamoto, Eisuke Sonomoto, Rikuho Takeda, Ryo Furukawa, Yusuke Muraki, Takafumi Shimizu, Kazuma Fukumura, Yuya Kanemoto, Takayuki Ito, and Shiyao Ding. 2026. Preference Estimation via Opponent Modeling in Multi-Agent Negotiation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28049–28058, San Diego, California, United States. Association for Computational Linguistics.