Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153244
Title: Deep learning and computer chess
Authors: Low, Benedict Yu
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Low, B. Y. (2021). Deep learning and computer chess. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153244
Abstract: This report presents a chess evaluation function trained using neural networks, without a priori knowledge of chess. The neural network undergoes two phases. In the first phase, it is trained using unsupervised learning to perform feature extraction. Subsequently in the second phase it undergoes supervised learning to compare two chess positions and select the more favourable one. The entire network is trained using only positions of a chess game and the game’s outcome, and no other information on chess. Although the neural network utilizes a relatively shallow network architecture by modern standards, it is capable of achieving very high accuracies and has shown great promise in its ability to identify key features that results in a favourable game. This project closely follows the implementation and concepts of DeepChess.
URI: https://hdl.handle.net/10356/153244
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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