@inproceedings{zhou-etal-2023-cast,
    title = "{I} Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons",
    author = "Zhou, Pei  and
      Zhu, Andrew  and
      Hu, Jennifer  and
      Pujara, Jay  and
      Ren, Xiang  and
      Callison-Burch, Chris  and
      Choi, Yejin  and
      Ammanabrolu, Prithviraj",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.624",
    doi = "10.18653/v1/2023.acl-long.624",
    pages = "11136--11155",
    abstract = "We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D{\&}D), a role-playing game, provides an ideal setting to investigate such interactions. Here, the Dungeon Master (DM), i.e., the teacher, guides the actions of several players{---}students, each with their own personas and abilities{---}to achieve shared goals grounded in a fantasy world. Our approach is to decompose and model these interactions into (1) the DM{'}s intent to guide players toward a given goal; (2) the DM{'}s guidance utterance to the players expressing this intent; and (3) a theory-of-mind (ToM) model that anticipates the players{'} reaction to the guidance one turn into the future. We develop a novel reinforcement learning (RL) method for training a DM that generates guidance for players by rewarding utterances where the intent matches the ToM-anticipated player actions. Human and automated evaluations show that a DM trained to explicitly model intents and incorporate ToM of the players using RL generates better-quality guidance that is 3x more likely to fulfill the DM{'}s intent than a vanilla natural language generation (NLG) approach.",
}

We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D{\&}D), a role-playing game, provides an ideal setting to investigate such interactions. Here, the Dungeon Master (DM), i.e., the teacher, guides the actions of several players{---}students, each with their own personas and abilities{---}to achieve shared goals grounded in a fantasy world. Our approach is to decompose and model these interactions into (1) the DM{'}s intent to guide players toward a given goal; (2) the DM{'}s guidance utterance to the players expressing this intent; and (3) a theory-of-mind (ToM) model that anticipates the players{'} reaction to the guidance one turn into the future. We develop a novel reinforcement learning (RL) method for training a DM that generates guidance for players by rewarding utterances where the intent matches the ToM-anticipated player actions. Human and automated evaluations show that a DM trained to explicitly model intents and incorporate ToM of the players using RL generates better-quality guidance that is 3x more likely to fulfill the DM{'}s intent than a vanilla natural language generation (NLG) approach.
Zhou, Pei Zhu, Andrew Hu, Jennifer Pujara, Jay Ren, Xiang Callison-Burch, Chris Choi, Yejin Ammanabrolu, Prithviraj
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons