  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.
