Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns.

翻译：在信息不对称且缺乏有效自然语言通信的情况下，开发能够与人类进行策略协同的自主智能体具有挑战性。本文提出一种共享控制博弈框架：两名玩家在信息不完全的条件下，通过交替回合共同操控一枚令牌以实现共同目标。我们针对该博弈中智能体与人类玩家协同的场景，构建了策略综合问题。为解决该问题，我们提出一种基于通信的方法，该方法包含语言模块与规划模块。语言模块将自然语言消息与有限标志集进行双向转换；该标志集作为紧凑的表示形式，用于捕捉玩家意图。规划模块利用这些标志，通过我们提出的非对称信息集蒙特卡洛树搜索与标志交换算法计算策略。我们在基于寻物迷宫棋盘游戏《黑夜精灵》的测试平台上评估了该方法的有效性。人体实验结果表明，通信能够缩小玩家间的信息差距，并以更少的回合数提升人-智能体协同效率。