Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language.
In this paper, we use a testbed based on Gnomes at Night, a search-and-find maze board game.
We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information.
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 (AISMCTS-F) algorithm we present.
