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 (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/144795 |
| _version_ | 1811690555816542208 |
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| author | Pereddy Vijai Krishna Reddy |
| author2 | He Ying |
| author_facet | He Ying Pereddy Vijai Krishna Reddy |
| author_sort | Pereddy Vijai Krishna Reddy |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:05:52Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/144795 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:05:52Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1447952020-11-25T01:11:04Z Deep learning and computer chess (part 1) Pereddy Vijai Krishna Reddy He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence 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. Bachelor of Engineering (Computer Engineering) 2020-11-25T01:11:04Z 2020-11-25T01:11:04Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/144795 en SCSE19-0592 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pereddy Vijai Krishna Reddy Deep learning and computer chess (part 1) |
| title | Deep learning and computer chess (part 1) |
| title_full | Deep learning and computer chess (part 1) |
| title_fullStr | Deep learning and computer chess (part 1) |
| title_full_unstemmed | Deep learning and computer chess (part 1) |
| title_short | Deep learning and computer chess (part 1) |
| title_sort | deep learning and computer chess part 1 |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/144795 |
| work_keys_str_mv | AT pereddyvijaikrishnareddy deeplearningandcomputerchesspart1 |