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Abstract <p>The article addresses the problem of creating dynamic narrative systems for video games with real-time interactivity. It presents the development and testing of a GPT integration component for dialogue generation, which revealed a critical limitation of cloud-based solutions with a 30-s latency unacceptable for gameplay. A hybrid architecture of an adaptive system is proposed, combining LLMs with reinforcement learning mechanisms. Particular attention is given to solving the problems of game world consistency and managing long-term context of NPC interactions through a RAG approach. The transition to the Edge AI paradigm with the application of quantization methods to achieve a target latency of 200–500 ms is substantiated. Metrics for evaluating personalization and dynamic content adaptation have been developed.</p>

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Development of an Adaptive System for Generating Game Quests and Dialogues Based on Large Language Models

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • V. T. Trofimchuk,
  • V. V. Kugurakova

摘要

Abstract

The article addresses the problem of creating dynamic narrative systems for video games with real-time interactivity. It presents the development and testing of a GPT integration component for dialogue generation, which revealed a critical limitation of cloud-based solutions with a 30-s latency unacceptable for gameplay. A hybrid architecture of an adaptive system is proposed, combining LLMs with reinforcement learning mechanisms. Particular attention is given to solving the problems of game world consistency and managing long-term context of NPC interactions through a RAG approach. The transition to the Edge AI paradigm with the application of quantization methods to achieve a target latency of 200–500 ms is substantiated. Metrics for evaluating personalization and dynamic content adaptation have been developed.