@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://preview.aclanthology.org/ingest-acl/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."
}