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.

在信息不对称的情况下，开发能够制定策略并与人类合作的自主代理人是具有挑战性的，需要有效的自然语言交流。我们引入了一个共享控制游戏，两个玩家轮流共同控制一个令牌，以在不完整信息下实现共同目标。我们对一个自主代理人在此游戏中与另一位玩家（人类）的政策综合问题进行了数学建模。为了解决这个问题，我们提出了一种基于通信的方法，包括一个语言模块和一个规划模块。语言模块将自然语言消息与定义玩家意图的有限标志集之间进行翻译。规划模块利用这些标志，使用我们提出的基于不对称信息集的蒙特卡洛树搜索与标志交换算法计算策略。我们使用《夜间侏儒》作为基础的测试场景进行了这种方法的有效性评估，该场景是一个搜索和找到迷宫棋盘游戏。人类主体实验的结果表明，通过交流，玩家之间的信息鸿沟变小，合作效率提高，并减少回合数。