{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:45:17Z","timestamp":1773967517459,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation","award":["IITP-2026-RS-2023-00254592"],"award-info":[{"award-number":["IITP-2026-RS-2023-00254592"]}]},{"name":"Joycity","award":["2024"],"award-info":[{"award-number":["2024"]}]},{"name":"IITP (Institute of Information & Communications Technology Planning & Evaluation)-ICAN","award":["IITP-2026-RS-2023-00260248"],"award-info":[{"award-number":["IITP-2026-RS-2023-00260248"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Procedural approaches have long been used in game development to reduce authoring costs and increase content diversity; however, traditional rule-based systems struggle to scale narrative complexity, whereas recent large language model (LLM)-based methods often produce outputs that are structurally invalid or incompatible with real-time game engines. This gap reflects a fundamental limitation in current practice: generative models lack systematic mechanisms for managing executable game knowledge rather than merely producing free-form narrative texts. To address this issue, we propose a Game Knowledge Management System (G-KMS) that reformulates LLM-based narrative generation as a structured knowledge management process. The proposed framework integrates knowledge grounding, schema-governed generation, normalization-based repair, engine-aligned knowledge admission, and application within a unified pipeline. The system was evaluated on a compact 2D Unity-based RPG benchmark using automated structural and semantic analyses, engine-level playability probes, and a controlled human player study. The experimental results demonstrated high reliability in knowledge admission, stable procedural structures, controlled expressive diversity, and a strong alignment between system-level metrics and player-perceived narrative quality, indicating that LLMs can function as dependable knowledge-construction components when embedded within a governed management pipeline. Beyond the evaluated RPG setting, this study suggests a practical and reproducible approach that may be extended to other executable systems, such as interactive simulations and training environments.<\/jats:p>","DOI":"10.3390\/systems14020175","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:13:01Z","timestamp":1770289981000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Game Knowledge Management System: Schema-Governed LLM Pipeline for Executable Narrative Generation in RPGs"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6109-4748","authenticated-orcid":false,"given":"Aynigar","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Dongguk University-Seoul, 30 Pildongro 1-gil, Jung-gu, Seoul 04620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0209-9231","authenticated-orcid":false,"given":"Aihe","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Autonomous Things Intelligence, Dongguk University-Seoul, 30 Pildongro 1-gil, Jung-gu, Seoul 04620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2219-0848","authenticated-orcid":false,"given":"Kyungeun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Advanced Convergence Engineering, Dongguk University-Seoul, 30 Pildongro 1-gil, Jung-gu, Seoul 04620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, C., Zhao, Y., and Liu, J. (2024, January 5\u20138). Game Generation via Large Language Models. Proceedings of the 2024 IEEE Conference on Games (CoG), Milan, Italy.","DOI":"10.1109\/CoG60054.2024.10645597"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, W., Wu, H., Jiang, L., Liu, X., Zhao, H., and Zhang, M. (2024). From Role-Play to Drama-Interaction: An LLM Solution. Findings of the Association for Computational Linguistics: ACL 2024, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2024.findings-acl.196"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, X., Xie, Z., and Jiang, S. (2025). Personalized Non-Player Characters: A Framework for Character-Consistent Dialogue Generation. AI, 6.","DOI":"10.3390\/ai6050093"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Park, J.S., O\u2019Brien, J., Cai, C.J., Morris, M.R., Liang, P., and Bernstein, M.S. (2023). Generative Agents: Interactive Simulacra of Human Behavior. UIST\u201923: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, San Francisco, CA, USA, 29 October\u20131 November 2023, Association for Computing Machinery.","DOI":"10.1145\/3586183.3606763"},{"key":"ref_5","first-page":"156","article-title":"PANGeA: Procedural Artificial Narrative Using Generative AI for Turn-Based, Role-Playing Video Games","volume":"20","author":"Buongiorno","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell. Interact. Digit. Entertain."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shao, Y., Li, L., Dai, J., and Qiu, X. (2023). Character-LLM: A Trainable Agent for Role-Playing. arXiv.","DOI":"10.18653\/v1\/2023.emnlp-main.814"},{"key":"ref_7","unstructured":"Kang, T., and Lin, M.C. (2025). Action2Dialogue: Generating Character-Centric Narratives from Scene-Level Prompts. arXiv."},{"key":"ref_8","unstructured":"Li, J., Li, Y., Wadhwa, N., Pritch, Y., Jacobs, D.E., Rubinstein, M., Bansal, M., and Ruiz, N. (2024). Unbounded: A Generative Infinite Game of Character Life Simulation. arXiv."},{"key":"ref_9","unstructured":"(2018). Knowledge Management Systems\u2014Requirements (Standard No. ISO 30401:2018)."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TCIAIG.2013.2286295","article-title":"A survey of real-time strategy game AI research and competition in StarCraft","volume":"5","author":"Synnaeve","year":"2013","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_11","first-page":"1","article-title":"An Architecture for Integrating Plan-Based Behavior Generation with Interactive Game Environments","volume":"1","author":"Young","year":"2004","journal-title":"J. Game Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TCIAIG.2011.2148116","article-title":"Search-Based Procedural Content Generation: A Taxonomy and Survey","volume":"3","author":"Togelius","year":"2011","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TCIAIG.2011.2159716","article-title":"Tanagra: Reactive Planning and Constraint Solving for Mixed-Initiative Level Design","volume":"3","author":"Smith","year":"2011","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alexander, R., and Martens, C. (2017). Deriving Quests from Open World Mechanics. FDG\u201917: Proceedings of the 12th International Conference on the Foundations of Digital Games, Hyannis, MA, USA, 14\u201317 August 2017, Association for Computing Machinery.","DOI":"10.1145\/3102071.3102098"},{"key":"ref_15","unstructured":"Burtenshaw, B., and Manjavacas, E. (2019). Toward Automated Quest Generation in Text-Adventure Games. Proceedings of the 4th Workshop on Computational Creativity in Language Generation, Tokyo, Japan, Association for Computational Linguistics."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100708","DOI":"10.1016\/j.entcom.2024.100708","article-title":"Managing and Controlling Digital Role-Playing Game Elements: A Current State of Affairs","volume":"51","author":"Maia","year":"2024","journal-title":"Entertain. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1038\/s42256-020-0208-z","article-title":"Increasing Generality in Machine Learning through Procedural Content Generation","volume":"2","author":"Risi","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_18","unstructured":"Koppen, L. (2024). Integrating a Human Feedback Loop in PCG for Level Design Using LLMs. [Bachelor\u2019s Thesis, University of Twente]."},{"key":"ref_19","first-page":"425","article-title":"Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI","volume":"19","author":"Sun","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell. Interact. Digit. Entertain."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kumaran, V., Carpenter, D., Rowe, J., Mott, B., and Lester, J. (2023, January 21\u201324). End-to-End Procedural Level Generation in Educational Games with Natural Language Instruction. Proceedings of the 2023 IEEE Conference on Games (CoG), Boston, MA, USA.","DOI":"10.1109\/CoG57401.2023.10333195"},{"key":"ref_21","unstructured":"Nasir, M.U., James, S., and Togelius, J. (2024). Word2World: Generating Stories and Worlds through Large Language Models. arXiv."},{"key":"ref_22","unstructured":"Li, W., Bai, Y., Lu, J., and Yi, K. (2022). Immersive Text Game and Personality Classification. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shuster, K., Urbanek, J., Szlam, A., and Weston, J. (2022). Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity. Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, USA, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2022.findings-naacl.182"},{"key":"ref_24","unstructured":"Jurafsky, D., Chai, J., Schluter, N., and Tetreault, J. (2020). Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, Association for Computational Linguistics."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ji, K., Lian, Y., Li, L., Gao, J., Li, W., and Dai, B. (2025). Enhancing Persona Consistency for LLMs\u2019 Role-Playing Using Persona-Aware Contrastive Learning. arXiv.","DOI":"10.18653\/v1\/2025.findings-acl.1344"},{"key":"ref_26","unstructured":"Takayama, J., Ohagi, M., Mizumoto, T., and Yoshikawa, K. (2025). Persona-Consistent Dialogue Generation via Pseudo Preference Tuning. Proceedings of the 31st International Conference on Computational Linguistics, Abu Dhabi, UAE, Association for Computational Linguistics."},{"key":"ref_27","unstructured":"Zhou, W., Peng, X., and Riedl, M. (2023). Dialogue Shaping: Empowering Agents through NPC Interaction. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jennings, N., Wang, H., Li, I., Smith, J., and Hartmann, B. (2024). What\u2019s the Game, Then? Opportunities and Challenges for Runtime Behavior Generation. UIST\u201924: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, Pittsburgh, PA, USA, 13\u201316 October 2024, Association for Computing Machinery.","DOI":"10.1145\/3654777.3676358"},{"key":"ref_29","unstructured":"Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., and Anandkumar, A. (2023). Voyager: An Open-Ended Embodied Agent with Large Language Models. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Meta Fundamental AI Research Diplomacy Team (FAIR), Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., and Hu, H. (2022). Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning. Science, 378, 1067\u20131074.","DOI":"10.1126\/science.ade9097"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"62581","DOI":"10.1109\/ACCESS.2024.3393485","article-title":"MemoryRepository for AI NPC","volume":"12","author":"Zheng","year":"2024","journal-title":"IEEE Access"},{"key":"ref_32","first-page":"54213","article-title":"MarioGPT: Open-Ended Text2Level Generation through Large Language Models","volume":"36","author":"Sudhakaran","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","unstructured":"Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J. (2019). Neural Text Generation with Unlikelihood Training. arXiv."},{"key":"ref_34","unstructured":"Chang, S., Wang, J., Dong, M., Pan, L., Zhu, H., Li, A.H., Lan, W., Zhang, S., Jiang, J., and Lilien, J. (2023). Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. arXiv."},{"key":"ref_35","unstructured":"Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y. (2020). The Curious Case of Neural Text Degeneration. arXiv."},{"key":"ref_36","unstructured":"Knight, K., Nenkova, A., and Rambow, O. (2016). A Diversity-Promoting Objective Function for Neural Conversation Models. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, Association for Computational Linguistics."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Reimers, N., and Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks. arXiv.","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Seraj, R., and Islam, S.M.S. (2020). The K-Means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9.","DOI":"10.3390\/electronics9081295"},{"key":"ref_39","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","unstructured":"McIntyre, N., and Lapata, M. (2010). Plot Induction and Evolutionary Search for Story Generation. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, Association for Computational Linguistics."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Iurgel, I.A., Zagalo, N., and Petta, P. (2009). Controlling Narrative Generation with Planning Trajectories: The Role of Constraints. Interactive Storytelling. ICIDS 2009, Springer.","DOI":"10.1007\/978-3-642-10643-9"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1613\/jair.2989","article-title":"Narrative Planning: Balancing Plot and Character","volume":"39","author":"Riedl","year":"2010","journal-title":"J. Artif. Intell. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shaker, N., Togelius, J., and Nelson, M.J. (2016). Procedural Content Generation in Games, Springer.","DOI":"10.1007\/978-3-319-42716-4"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TG.2018.2846639","article-title":"Procedural Content Generation via Machine Learning (PCGML)","volume":"10","author":"Summerville","year":"2018","journal-title":"IEEE Trans. Games"},{"key":"ref_45","unstructured":"Smith, G., Othenin-Girard, A., Whitehead, J., and Wardrip-Fruin, N. (June, January 29). PCG-Based Game Design: Creating Endless Web. Proceedings of the International Conference on the Foundations of Digital Games, Raleigh, NC, USA."},{"key":"ref_46","unstructured":"Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., and Xing, E.P. (2023, January 10\u201316). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. Proceedings of the 37th International Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_47","unstructured":"Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., and Henighan, T. (2022). Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/2\/175\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:32:58Z","timestamp":1770291178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/2\/175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["systems14020175"],"URL":"https:\/\/doi.org\/10.3390\/systems14020175","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]}}}