Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games

• URL: http://arxiv.org/abs/2510.18395v1
• Date: Tue, 21 Oct 2025 08:15:04 GMT
• Title: Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games
• Authors: Runnan Qi, Yanan Ni, Lumin Jiang, Zongyuan Li, Kuihua Huang, Xian Guo,
• Abstract summary: Memory-Augmented State Machine Prompting (MASMP) is a novel framework for LLM agents in real-time strategy games.<n>MASMP integrates state machine prompting with memory mechanisms to unify structured actions with long-term tactical coherence.
• Score: 4.203733214192822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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