A Study on LLM-based Game NPC State Control using FSM

Q: What does this research mean for the field?

Integrating a Finite State Machine (FSM)-based guardrail with Large Language Model (LLM)-based game NPCs reduces unauthorized state transitions and improves the success rate of valid inputs compared to using LLMs alone. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Yunseong Na; Youngju Nam

doi:10.14801/jkiit.2026.24.8.81

August 31, 2026The Journal of Korean Institute of Information Technology

A Study on LLM-based Game NPC State Control using FSM

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Key Points

Develop and assess an FSM-based guardrail architecture that prevents unauthorized game state transitions caused by LLM-driven non-player characters while preserving dynamic dialogue capabilities.
Structured an architecture where an LLM converts natural language player inputs into JSON-formatted intent and ruleTag data.
Programmed a finite state machine to validate current game states against parsed rule tags before authorizing or blocking state transitions.
Evaluated system behavior by benchmarking an LLM-only approach against the hybrid LLM+FSM model within a Unity-based interaction environment.
Reduced the occurrence of prohibited state transitions in response to invalid player inputs when using the LLM+FSM guardrail.
Increased the execution success rate for valid, normal player inputs compared with the baseline LLM-only setup.

Implication

Comparative analysis demonstrates reduced invalid state transitions in game NPCs using an FSM guardrail, highlighting safer LLM integration into gameplay systems.

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Cite This Study

Na et al. (2026) studied this question.

synapsesocial.com/papers/6a9934cf636c6408cfa7c81c https://doi.org/10.14801/jkiit.2026.24.8.81

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A Study on LLM-based Game NPC State Control using FSM

Key Points

Implication

Abstract

Discussion

Cite This Study

A Study on LLM-based Game NPC State Control using FSM

Key Points

Implication

Abstract

Discussion

Cite This Study

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