@inproceedings{yamasaki-etal-2025-multi,
    title = "Multi-step or Direct: A Proactive Home-Assistant System Based on Commonsense Reasoning",
    author = "Yamasaki, Konosuke  and
      Tanaka, Shohei  and
      Yuguchi, Akishige  and
      Kawano, Seiya  and
      Yoshino, Koichiro",
    editor = "B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Lef{\`e}vre, Fabrice  and
      Asher, Nicholas  and
      Kim, Seokhwan  and
      Merlin, Teva",
    booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2025",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-10/2025.sigdial-1.45/",
    pages = "561--572",
    abstract = "There is a growing expectation for the realization of proactive home-assistant robots that can assist users in their daily lives. It is essential to develop a framework that closely observes the user{'}s surrounding context, selectively extracts relevant information, and infers the user{'}s needs to proactively propose appropriate assistance. In this study, we first extend the Do-I-Demand dataset to define expected proactive assistance actions in domestic situations, where users make ambiguous utterances. These behaviors were defined based on common patterns of support that a majority of users would expect from a robot. We subsequently constructed a framework that infers users' expected assistance actions from ambiguous utterances through commonsense reasoning. We explored two approaches: (1) multi-step reasoning using COMET as a commonsense reasoning engine, and (2) direct reasoning using large language models. Our experimental results suggest that both the multi-step and direct reasoning methods can successfully derive necessary assistance actions even when dealing with ambiguous user utterances."
}