Artificial Intelligence (AI) Integration in Khmer Chess (Ouk Chaktrang) Game Development

Abstract

This paper focuses on developing an AI-based Khmer Chess (Ouk Chaktrang) game using the Python language, without relying on external libraries or frameworks. The project aims to create a Python package that can be used to develop various platforms, providing an accessible and user-friendly game that highlights the unique aspects of Khmer chess, while also serving as a foundation for future AI development in this area. The core of the AI implementation employs the Minimax algorithm combined with alpha-beta pruning to enable the AI to make strategic decisions by evaluating possible moves. Key strengths of this project include a functional game engine with features like move generation and check detection using an efficient reverse-check method. The development uses a 2D array for board representation and implements a piece scoring system. The study also demonstrates its versatility by being implemented in both console and GUI versions. However, key limitations and areas for improvement are identified. The analysis shows a significant increase in computational time as the AI's search depth increases, particularly at higher depths. The AI's move selection is currently random from the list of best moves, leading to unpredictable decisions. Moreover, the AI does not employ advanced techniques such as iterative deepening, selective search, or machine learning due to a lack of training data. In conclusion, this project serves as a foundational benchmark for future development in Khmer Chess AI and aims to promote Cambodian cultural heritage by showcasing the traditional game of Ouk Chaktrang. By addressing the limitations and implementing the proposed enhancements, the project has the potential to become an even more robust and engaging tool for preserving this culturally significant game.