We propose a novel task, G4C (Goal-driven Guidance Generation in Grounded Communication), for studying goal-driven and grounded natural language interactions. Specifically, we choose Dungeons and Dragons (D&D) -- a role-playing game consisting of multiple player characters and a Dungeon Master (DM) who collaborate to achieve a set of goals that are beneficial to the players -- as a testbed for this task. Here, each of the player characters is a student, with their own personas and abilities, and the DM is the teacher, an arbitrator of the rules of the world and responsible for assisting and guiding the students towards a global goal. We propose a theory-of-mind-inspired methodology for training such a DM with reinforcement learning (RL), where a DM: (1) learns to predict how the players will react to its utterances using a dataset of D&D dialogue transcripts; and (2) uses this prediction as a reward function providing feedback on how effective these utterances are at guiding the players towards a goal. Human and automated evaluations show that a DM trained with RL to generate guidance by incorporating a theory-of-mind of the players significantly improves the players' ability to achieve goals grounded in their shared world.

该研究提出了一个基于角色扮演游戏Dungeons and Dragons的新任务G4C（Goal-driven Guidance Generation in Grounded Communication），通过满足学习者的目标来培养具有人类思维想象力的DM（Dungeon Master），实现在基于语境的自然语言交互中DM对学生进行有效指导的训练方法。

一名AI地牢大师的指南：学习在龙与地下城（Dungeons and Dragons）中使用意图和心智理论进行对话和指导