The Development of Train Artificial Intelligence (AI) Model for Bagapit Chess (Catur Bagapit) Engine using Random Forest Regressor Algorithm : a Traditional Game from Kalimantan, Indonesia

Dwi Hastuti; Harits Ar Rosyid; M. Zainal Arifin

doi:10.36456/eab74t67

Authors

Dwi Hastuti Universitas Negeri Malang https://orcid.org/0000-0001-8716-7062
Harits Ar Rosyid Universitas Negeri Malang
M. Zainal Arifin Universitas Negeri Malang

DOI:

https://doi.org/10.36456/eab74t67

Keywords:

Bagapit Chess,, Bagapit Chess Engine, Artificial Intelligence, Random Forest Regressor, Negamax with Alpha-Beta Pruning, Machine Learning

Abstract

Bagapit Chess (Catur Bagapit) is a traditional strategy board game originating from the Kalimantan region of Indonesia. Despite its rich cultural heritage and strategic depth comparable to international Chess, Bagapit Chess remains largely unstudied from a computational intelligence perspective. This paper presents the development of an Artificial Intelligence (AI) model for the Bagapit Chess engine using the Random Forest Regressor (RFR) algorithm. The AI model is trained to evaluate board positions and generate competitive move decisions through a heuristic evaluation function augmented by machine learning. A dataset of 15,000 annotated game positions was constructed from expert gameplay, encoding board features including piece Material Advantage, Chess Movement, Defense Stance, mobility, and Attack Coverage across the 8×8 Bagapit board. The Random Forest Regressor model was integrated with a Negamax search tree enhanced by Alpha-Beta Pruning to achieve efficient and intelligent move selection. The trained model achieved an R² score of 0.9134, a Mean Absolute Error (MAE) of 0.0872, and a Root Mean Squared Error (RMSE) of 0.1104 on the test set. In engine evaluation against a rule-based baseline, the AI model won 84.2% of games under standard time control. This study contributes to the digitalization and preservation of Indonesian traditional games and demonstrates the applicability of ensemble machine learning to non-standard board game engines.

References

[1] Z. Shuhaida Abdull Rahman, N. Ismail, B. Mambarasi Nehe, N. Labanihuda Abdull Rahman, A. Ramli, and K. Pengajian Seni Kreatif Universiti Teknologi MARA Shah Alam, “The Challenges Preserving Traditional Games in Malaysia and Indonesia,” pp. 347–362, May 2025, doi: 10.2991/978-2-38476-406-8_26.

[2] H. Reginald, J. Yang, C. Y. Dendeng, and P. A. Suri, “From Tradition to Technology: Exploring Intergenerational Responses to a Benteng-Bentengan Video Game,” Procedia Comput Sci, vol. 269, no. 5, pp. 732–740, Jan. 2025, doi: 10.1016/j.procs.2025.09.016.

[3] R. R. Varghese, D. R. Aiswarya, A. Roy, V. Muraly, and S. Renjith, "A Novel Approach to Predict Success of Online Games Using Random Forest Regressor for Time Series Data," in Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering, vol. 881, Springer, Singapore, 2022. https://doi.org/10.1007/978-981-19-1111-8_3

[4] D. Silver et al., "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm," arXiv preprint arXiv:1712.01815, 2017. [Related review available via DeepMind publications: https://www.maiachess.com/]

[5] R. Singh, A. Kumar, P. Trivedi, and M. Rathore, "CHESS AI: Machine Learning and Negamax Based Chess Engine," in Proc. 2023 IEEE Conference, IEEE Xplore, 2023. https://ieeexplore.ieee.org/document/10080746/

[6] N. Surjanovic, D. Bai, T. B. Murphy, and A. Bouchard-Côté, "Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests," arXiv preprint, 2024. [Available: https://arxiv.org/abs/2107.12501 for related RF classifier game design work]

[7] Scientific Reports, "Enhancing Software Effort Estimation with Random Forest Tuning and Adaptive Decision Strategies," Nature/Scientific Reports, 2025. https://www.nature.com/articles/s41598-025-14372-7

[8] H. Choudhary, A. Inamdar, S. Kharade, A. Maheshwari, and P. Kale, "Comparative Analysis of Negamax Algorithm with Alpha–Beta Pruning Optimization for Chess Engine," in Advanced Computational and Communication Paradigms, ICACCP 2023, Lecture Notes in Networks and Systems, vol. 535, Springer, Singapore, 2023. https://doi.org/10.1007/978-981-99-4284-8_19

[9] S. Omidshafiei et al., "Navigating the Landscape of Multiplayer Games," Nature Communications, vol. 11, no. 5603, 2020. https://www.nature.com/articles/s41467-020-19244-4

[10] J. Goodman, A. Wallat, D. Perez-Liebana, and S. Lucas, "A Case Study in AI-Assisted Board Game Design," in Proc. 2023 IEEE Conference on Games (CoG), 2023. https://doi.org/10.1109/CoG57401.2023.10333138

[11] N. Politowski, F. Petrillo, G. El Boussaidi, G. Ullmann, and Y. Guéhéneuc, "Assessing Video Game Balance using Autonomous Agents," in Proc. 2023 IEEE/ACM 7th International Workshop on Games and Software Engineering (GAS), 2023. https://doi.org/10.1109/GAS59301.2023.00011

[12] M. Balla, G. Long, D. Jeurissen, J. Goodman, R. Gaina, and D. Perez-Liebana, "PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games," in Proc. 2023 IEEE Conference on Games (CoG), 2023. https://doi.org/10.1109/CoG57401.2023.10333223

[13] B. Liu, C. Cheng, and Y. Zhao, "Games for Artificial Intelligence Research: A Review and Perspectives," arXiv:2304.13269v4, IEEE Transactions on Games, 2024. https://arxiv.org/html/2304.13269v4

[14] Adversarial Random Forest Classifier for Automated Game Design, arXiv preprint, arXiv:2107.12501, 2021. https://arxiv.org/abs/2107.12501

[15] T. Constant and G. Levieux, "Automated Evaluation of Game Experience based on Game Dynamics and Motives for Play," in Extended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play, 2022. https://doi.org/10.1145/3505270.3558343

[16] A. Albaghajati and M. Ahmed, "Video Game Automated Testing Approaches: An Assessment Framework," IEEE Transactions on Games, vol. 15, no. 1, pp. 81–94, Mar. 2023. https://doi.org/10.1109/TG.2020.3032796

[17] House Price Prediction Analysis Using Linear Regression and Random Forest — Comparative Analysis of MAE, RMSE, R², MAPE, IOINFORMATIC Journal of AI Engineering Applications, 2024. https://www.ioinformatic.org/index.php/JAIEA/article/download/1047/879

[18] Ma W., "Optimization of Alpha-Beta Pruning Based on Heuristic Algorithm," related review in ProjectPro AI documentation, 2023. https://www.projectpro.io/article/alpha-beta-pruning-in-ai/1157