Generating dynamic and lifelike NPC dialogs in role-playing games using large language model
Huang, Junyang (2024)
Kandidaatintyö
2024
School of Engineering Science, Tietotekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2024061250780
https://urn.fi/URN:NBN:fi-fe2024061250780
Tiivistelmä
Traditional non-player character dialog systems rely on preset lines, which lack realism and dynamics and reduce player immersion. This study aims to explore overcoming these limitations through large language models.
This paper reviews existing non-player character dialogue systems and emphasizes the need to generate dynamic, context-sensitive dialogs. The capabilities of large language models, especially GPT-3 and GPT-4, in generating coherent, contextually relevant content are explored. In this paper, I propose a framework for non-player role dialog based on large language model. The framework includes key components such as intent recognition, memory retrieval, dialog management, and response generation. The framework was tested in practice in the text-based role-playing game Call of Cthulhu. Evaluations from game participants indicated that the dialog based on the large language model was more natural and contextualized, enhancing the overall game experience.
This study demonstrates the feasibility and advantages of integrating large language models into non-player character dialog systems, providing valuable exploration and practice for developing smarter and more responsive game characters. However, there are still challenges in managing long-term memory and context in non-player character dialog systems based on large language models, which require further research.
This paper reviews existing non-player character dialogue systems and emphasizes the need to generate dynamic, context-sensitive dialogs. The capabilities of large language models, especially GPT-3 and GPT-4, in generating coherent, contextually relevant content are explored. In this paper, I propose a framework for non-player role dialog based on large language model. The framework includes key components such as intent recognition, memory retrieval, dialog management, and response generation. The framework was tested in practice in the text-based role-playing game Call of Cthulhu. Evaluations from game participants indicated that the dialog based on the large language model was more natural and contextualized, enhancing the overall game experience.
This study demonstrates the feasibility and advantages of integrating large language models into non-player character dialog systems, providing valuable exploration and practice for developing smarter and more responsive game characters. However, there are still challenges in managing long-term memory and context in non-player character dialog systems based on large language models, which require further research.