mlsanityData to 14 Oct 2026

Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4

arXiv 2309.17277 ↗First posted 29 Sep 2023cs.AIcited by 68

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

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 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.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 56 of its 97 references that are in the map. First posted on arXiv 29 Sep 2023.

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56 of 97 references.

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  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Models25 May

Other references · 55

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Citing papers per year: 2023: 4; 2024: 27; 2025: 18; 2026: 19.

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