Deep learning and computer chess (part 1)
In this paper, a supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions has been implemented. The architecture used to build the ANN model is based on the architecture mentioned in (Matthew Lai, 2015) paper [1]. The methods that are implemented aim...
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Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2020
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Online Access: | https://hdl.handle.net/10356/144795 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In this paper, a supervised learning approach for training Artificial Neural Networks (ANNs) to
evaluate chess positions has been implemented. The architecture used to build the ANN model is
based on the architecture mentioned in (Matthew Lai, 2015) paper [1]. The methods that are
implemented aim to train ANN architecture to understand chess moves and techniques in a manner
similar to how chess grandmasters would.
We collected over 17,00,000 different chess game positions played by highly skilled chess players.
We then used the evaluation function of stockfish, one of the strongest existing chess engines, to
label the games. We extracted the positions from the games using Forsyth-Edwards notation and
stored them in 2 different files which are later used for training the model after preprocessing the
data. The results show how simple Multilayer Perceptrons (MLPs) perform with varying depth of the
network. |
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