  Unlike perfect information games, where all elements are known to every
player, imperfect information games emulate the real-world complexities of
decision-making under uncertain or incomplete information. GPT-4, the recent
breakthrough in large language models (LLMs) trained on massive passive data,
is notable for its knowledge retrieval and reasoning abilities. This paper
delves into the applicability of GPT-4's learned knowledge for imperfect
information games. To achieve this, we introduce \textbf{Suspicion-Agent}, an
innovative agent that leverages GPT-4's capabilities for performing in
imperfect information games. With proper prompt engineering to achieve
different functions, Suspicion-Agent based on GPT-4 demonstrates remarkable
adaptability across a range of imperfect information card games. Importantly,
GPT-4 displays a strong high-order theory of mind (ToM) capacity, meaning it
can understand others and intentionally impact others' behavior. Leveraging
this, we design a planning strategy that enables GPT-4 to competently play
against different opponents, adapting its gameplay style as needed, while
requiring only the game rules and descriptions of observations as input. In the
experiments, we qualitatively showcase the capabilities of Suspicion-Agent
across three different imperfect information games and then quantitatively
evaluate it in Leduc Hold'em. The results show that Suspicion-Agent can
potentially outperform traditional algorithms designed for imperfect
information games, without any specialized training or examples. In order to
encourage and foster deeper insights within the community, we make our
game-related data publicly available.
