Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162874
Title: Deep learning and computer chess
Authors: Ding, CongCong
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Ding, C. (2022). Deep learning and computer chess. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162874
Abstract: This report presents two supervised learning approach for training neural networks to evaluate chess positions. The architecture used to build the neural network model is based on the Giraffe’s architecture [2] and Stockfish NNUE -HalfKP [3]. Implemented a method to train a neural network architecture to understand chess movement and techniques that a grandmaster would play. Both approaches implemented as a 7-class classification problem on a dataset of over 10,000 samples games. We collected different chess game played by grandmaster, then used the evaluation function of stockfish [5], one of the strongest existing chess engines, to get the score of the positions and label it accordingly. We extracted the positions from the games using Forsyth-Edwards notation and stored them in csv files which are later used for training the model.
URI: https://hdl.handle.net/10356/162874
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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