Abstract
Chess is a two-player strategy board game played on a chessboard, a checkered game board with 64 squares arranged in an 8 × 8 grid. The current technological advancements have transformed the way we not only play chess but study chess techniques and analyze the beautiful game. Predicting the next move in a chess game is not an easy task, as the branching factor of Chess on average is 35. Thus, new algorithms need to be researched to make an ideal Game Engine. In this paper, we analyze why the traditional algorithms fail to work for a Chess Engine and create one such engine which evaluates the Game Tree using the traditional Alpha–Beta Algorithm, enhanced by using Iterative Deepening Search.
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Parashar, A., Jha, A.K., Kumar, M. (2022). Analyzing a Chess Engine Based on Alpha–Beta Pruning, Enhanced with Iterative Deepening. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Expert Clouds and Applications. Lecture Notes in Networks and Systems, vol 444. Springer, Singapore. https://doi.org/10.1007/978-981-19-2500-9_51
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