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- W3035394992 abstract "The adaptability and competitiveness of neural networks has been the focus of much research. The most popular applications of neural networks are in classic games like 2D chess. The focus of this paper is to study the adaptability of a neural network that was trained on chess boards of different dimensions (2,3). We define and formalize chess in N-dimensions, including board, pieces and legal moves. The paper compares a classic chess algorithm as a benchmark for both training and checking the progress of the neural network. We provide an experimental comparison of the two algorithms playing N-dimensional chess." @default.
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- W3035394992 date "2019-05-01" @default.
- W3035394992 modified "2023-09-25" @default.
- W3035394992 title "Neural Network adaptability from 2D to 3D Chess" @default.
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- W3035394992 doi "https://doi.org/10.1109/saci46893.2019.9111577" @default.
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