Article Sidebar

Download
PDF
Statistic
Read Counter : 249 Download : 165

Main Article Content

Abstract

The preservation of residential architecture from traditional ethnic groups has never faced the types of challenges it does today due to urbanization. These challenges include the insufficient retention of landmarks due to competing stakeholder interests, which often leads to irreversible loss of cultural heritage. This research proposes a new culturally identity-oriented multi-agent reinforcement learning system for the protection of Bai ethnic traditional dwellings in Dali, Yunnan Province. The research combines diverse multi-source data collection approaches, including the building’s architecture and culture, urbanization statistics, and stakeholder networks, and develops an advanced computational framework in which every stakeholder category is embedded as an independent intelligent agent with specific behavioral patterns and autonomous decision-making skills. Specialized deep Q-networks of enhanced Q-value methods that consider cultural identity loss in Q-value calculus through loss function adjustments aimed at balancing cultural preservation and stakeholder appeasement were employed within the framework. Implementation results show performance with an overall accuracy of 89.3% for implementation and 87.2% for cultural preservation effectiveness. Conventional approaches previously achieved significantly lower accuracy within these parameters, 15-25 percentage points. Enhancements in cultural identity increase from a baseline of 58.3% to optimized values of 91.2%, while community satisfaction improves from 54.7% to 86.4%. The framework maintains coordination indices above 85% for all stakeholder groups, showing scalability with over 85% replication success rates for populations between 5,000 and 50,000 residents. This demonstrates theoretical and practical value in the use of AI concerning culturally aware heritage preservation.

Keywords

Multi-agent reinforcement learning Cultural heritage preservation Cultural identity Traditional architecture Stakeholder coordination

Article Details

Author Biographies

Xiaohua Qian, Faculty of Social Sciences and Humanities,Universiti Malaysia Sarawak, 94300 Kota Samarahan,Sarawak, Malaysia

XIAO-HUA QIAN is currently pursuing his PhD at the Universiti Malaysia Sarawak, Malaysia, where his research interests are in community relations of ethnic minorities and the preservation of traditional dwellings.

Bemen Wong Win Keong, Faculty of Social Sciences and Humanities,Universiti Malaysia Sarawak, 94300 Kota Samarahan,Sarawak, Malaysia

Faculty of Social Sciences and Humanities,Universiti Malaysia Sarawak, 94300 Kota Samarahan,Sarawak, Malaysia.

How to Cite
Qian, X., Ilan Mersat, N., Hashim, H., & Wong Win Keong, B. (2025). Multi-agent reinforcement learning for Bai ethnic traditional dwelling protection in Dali: cultural identity-oriented community relationship optimization and urbanization adaptation algorithm . Future Technology, 4(4), 33–42. Retrieved from https://fupubco.com/futech/article/view/427
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. M. Al‐Raeei, "The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization," Sustainable development, vol. 33, no. 1, pp. 508-517, 2025. DOI: https://doi.org/10.1002/sd.3131
  2. F. Cugurullo, F. Caprotti, M. Cook, A. Karvonen, P. MᶜGuirk, and S. Marvin, "The rise of AI urbanism in post-smart cities: A critical commentary on urban artificial intelligence," Urban Studies, vol. 61, no. 6, pp. 1168-1182, 2024.
  3. A. Wong, T. Bäck, A. V. Kononova, and A. Plaat, "Deep multiagent reinforcement learning: Challenges and directions," Artificial Intelligence Review, vol. 56, no. 6, pp. 5023-5056, 2023. DOI:
  4. https://doi.org/10.1007/s10462-022-10299-x
  5. S. Gronauer and K. Diepold, "Multi-agent deep reinforcement learning: a survey," Artificial Intelligence Review, vol. 55, no. 2, pp. 895-943, 2022. DOI: https://doi.org/10.1007/s10462-021-09996-w
  6. W. Li et al., "Systematic review: a scientometric analysis of the status, trends and challenges in the application of digital technology to cultural heritage conservation (2019–2024)," npj Heritage Science, vol. 13, no. 1, p. 90, 2025. DOI: https://doi.org/10.1038/s40494-025-01636-8
  7. J. Xie, "Innovative design of artificial intelligence in intangible cultural heritage," Scientific Programming, vol. 2022, no. 1, p. 6913046, 2022. DOI: https://doi.org/10.1155/2022/6913046
  8. M. Carrozzino and M. Bergamasco, "Beyond virtual museums: Experiencing immersive virtual reality in real museums," Journal of cultural heritage, vol. 11, no. 4, pp. 452-458, 2010. DOI: https://doi.org/10.1016/j.culher.2010.04.001
  9. Z. Ning and L. Xie, "A survey on multi-agent reinforcement learning and its application," Journal of Automation and Intelligence, vol. 3, no. 2, pp. 73-91, 2024. DOI: https://doi.org/10.1016/j.jai.2024.02.003
  10. B. Peng et al., "Facmac: Factored multi-agent centralised policy gradients," Advances in Neural Information Processing Systems, vol. 34, pp. 12208-12221, 2021.
  11. K. Zhang, Z. Yang, and T. Başar, "Multi-agent reinforcement learning: A selective overview of theories and algorithms," Handbook of reinforcement learning and control, pp. 321-384, 2021. DOI: https://doi.org/10.1007/978-3-030-60990-0_12
  12. D. Shao, K. Zoh, and Y. Xie, "The spatial differentiation mechanism of intangible cultural heritage and its integration with tourism development based on explainable machine learning and coupled coordination models: a case study of the Jiang-Zhe-Hu in China," Heritage Science, vol. 12, no. 1, pp. 1-22, 2024. DOI: https://doi.org/10.1186/s40494-024-01528-3
  13. T. H. Son, Z. Weedon, T. Yigitcanlar, T. Sanchez, J. M. Corchado, and R. Mehmood, "Algorithmic urban planning for smart and sustainable development: Systematic review of the literature," Sustainable Cities and Society, vol. 94, p. 104562, 2023. DOI: https://doi.org/10.1016/j.scs.2023.104562
  14. M. B. Prados-Peña, G. Pavlidis, and A. García-López, "New technologies for the conservation and preservation of cultural heritage through a bibliometric analysis," Journal of Cultural Heritage Management and Sustainable Development, vol. 15, no. 3, pp. 664-686, 2025. DOI: https://doi.org/10.1108/JCHMSD-07-2022-0124
  15. J. Otero, "Heritage conservation future: Where we stand, challenges ahead, and a paradigm shift," Global Challenges, vol. 6, no. 1, p. 2100084, 2022. DOI: https://doi.org/10.1002/gch2.202100084
  16. L. Zhou, S. Pan, J. Wang, and A. V. Vasilakos, "Machine learning on big data: Opportunities and challenges," Neurocomputing, vol. 237, pp. 350-361, 2017. DOI: https://doi.org/10.1016/j.neucom.2017.01.026
  17. Q. Weng et al., "How will ai transform urban observing, sensing, imaging, and mapping?," npj Urban Sustainability, vol. 4, no. 1, p. 50, 2024. DOI: https://doi.org/10.1038/s42949-024-00188-3
  18. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural networks, vol. 61, pp. 85-117, 2015. DOI: https://doi.org/10.1016/j.neunet.2014.09.003
  19. F. Bahrpeyma and D. Reichelt, "A review of the applications of multi-agent reinforcement learning in smart factories," Frontiers in Robotics and AI, vol. 9, p. 1027340, 2022. DOI: https://doi.org/10.3389/frobt.2022.1027340
  20. M. Fiorucci, M. Khoroshiltseva, M. Pontil, A. Traviglia, A. Del Bue, and S. James, "Machine learning for cultural heritage: A survey," Pattern Recognition Letters, vol. 133, pp. 102-108, 2020. DOI: https://doi.org/10.1016/j.patrec.2020.02.017
  21. Y. Li, L. Zhao, Y. Chen, N. Zhang, H. Fan, and Z. Zhang, "3D LiDAR and multi-technology collaboration for preservation of built heritage in China: A review," International Journal of Applied Earth Observation and Geoinformation, vol. 116, p. 103156, 2023. DOI: https://doi.org/10.1016/j.jag.2022.103156
  22. F. Noardo, "Architectural heritage semantic 3D documentation in multi-scale standard maps," Journal of Cultural heritage, vol. 32, pp. 156-165, 2018. DOI: https://doi.org/10.1016/j.culher.2018.02.009
  23. A. Tampuu et al., "Multiagent cooperation and competition with deep reinforcement learning," PloS one, vol. 12, no. 4, p. e0172395, 2017. DOI: https://doi.org/10.1371/journal.pone.0172395
  24. R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, "Multi-agent actor-critic for mixed cooperative-competitive environments," Advances in neural information processing systems, vol. 30, 2017.
  25. Z. Luo, Z. Chen, and J. Welsh, "Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors," arXiv preprint arXiv:2406.07848, 2024. DOI: https://doi.org/10.1049/ell2.70342
  26. V. Mnih et al., "Human-level control through deep reinforcement learning," nature, vol. 518, no. 7540, pp. 529-533, 2015. DOI: https://doi.org/10.1038/nature14236
  27. H. Hassani, S. Nikan, and A. Shami, "Improved exploration–exploitation trade-off through adaptive prioritized experience replay," Neurocomputing, vol. 614, p. 128836, 2025. DOI: https://doi.org/10.1016/j.neucom.2024.128836
  28. A. Kumar, J. Fu, M. Soh, G. Tucker, and S. Levine, "Stabilizing off-policy q-learning via bootstrapping error reduction," Advances in neural information processing systems, vol. 32, 2019.
  29. A. B. Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information fusion, vol. 58, pp. 82-115, 2020. DOI: https://doi.org/10.1016/j.inffus.2019.12.012
  30. G. Colavizza, T. Blanke, C. Jeurgens, and J. Noordegraaf, "Archives and AI: An overview of current debates and future perspectives," ACM Journal on Computing and Cultural Heritage (JOCCH), vol. 15, no. 1, pp. 1-15, 2021. DOI: https://doi.org/10.1145/347901
  31. S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep learning based recommender system: A survey and new perspectives," ACM computing surveys (CSUR), vol. 52, no. 1, pp. 1-38, 2019. DOI: https://doi.org/10.1145/3285029
  32. L. Stacchio et al., "An ethical framework for trustworthy Neural Rendering applied in cultural heritage and creative industries," Frontiers in Computer Science, vol. 6, p. 1459807, 2024. DOI: https://doi.org/10.3389/fcomp.2024.1459807
  33. D. E. Neves, L. Ishitani, and Z. K. G. do Patrocínio Júnior, "Advances and challenges in learning from experience replay," Artificial Intelligence Review, vol. 58, no. 2, p. 54, 2024. DOI: https://doi.org/10.1007/s10462-024-11062-0