Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911223610605568 |
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| author | Qi, Runnan Ni, Yanan Jiang, Lumin Li, Zongyuan Huang, Kuihua Guo, Xian |
| author_facet | Qi, Runnan Ni, Yanan Jiang, Lumin Li, Zongyuan Huang, Kuihua Guo, Xian |
| contents | This paper proposes Memory-Augmented State Machine Prompting (MASMP), a novel framework for LLM agents in real-time strategy games. Addressing key challenges like hallucinations and fragmented decision-making in existing approaches, MASMP integrates state machine prompting with memory mechanisms to unify structured actions with long-term tactical coherence. The framework features: (1) a natural language-driven state machine architecture that guides LLMs to emulate finite state machines and behavior trees through prompts, and (2) a lightweight memory module preserving strategic variables (e.g., tactics, priority units) across decision cycles. Experiments in StarCraft II demonstrate MASMP's 60% win rate against the hardest built-in AI (Lv7), vastly outperforming baselines (0%). Case studies reveal the method retains LLMs' semantic comprehension while resolving the "Knowing-Doing Gap" through strict state-action mapping, achieving both interpretability and FSM-like reliability. This work establishes a new paradigm for combining neural and symbolic AI in complex decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games Qi, Runnan Ni, Yanan Jiang, Lumin Li, Zongyuan Huang, Kuihua Guo, Xian Artificial Intelligence 68T42, 68T37, 91A35 I.2.6; I.2.11; I.2.8; K.8.0 This paper proposes Memory-Augmented State Machine Prompting (MASMP), a novel framework for LLM agents in real-time strategy games. Addressing key challenges like hallucinations and fragmented decision-making in existing approaches, MASMP integrates state machine prompting with memory mechanisms to unify structured actions with long-term tactical coherence. The framework features: (1) a natural language-driven state machine architecture that guides LLMs to emulate finite state machines and behavior trees through prompts, and (2) a lightweight memory module preserving strategic variables (e.g., tactics, priority units) across decision cycles. Experiments in StarCraft II demonstrate MASMP's 60% win rate against the hardest built-in AI (Lv7), vastly outperforming baselines (0%). Case studies reveal the method retains LLMs' semantic comprehension while resolving the "Knowing-Doing Gap" through strict state-action mapping, achieving both interpretability and FSM-like reliability. This work establishes a new paradigm for combining neural and symbolic AI in complex decision-making. |
| title | Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games |
| topic | Artificial Intelligence 68T42, 68T37, 91A35 I.2.6; I.2.11; I.2.8; K.8.0 |
| url | https://arxiv.org/abs/2510.18395 |