Deep learning for computer chess (part 1)
This report encompasses the implementation of two state-of-the-art machine learning algorithms for evaluating chess positions. The first algorithm makes use of artificial neural networks and manual feature representation thus closely following the implementation and architecture of Matthew Lai’s Gir...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157572 |
| _version_ | 1811687447320330240 |
|---|---|
| author | Arora, Manav |
| author2 | He Ying |
| author_facet | He Ying Arora, Manav |
| author_sort | Arora, Manav |
| collection | NTU |
| description | This report encompasses the implementation of two state-of-the-art machine learning algorithms for evaluating chess positions. The first algorithm makes use of artificial neural networks and manual feature representation thus closely following the implementation and architecture of Matthew Lai’s Giraffe. Giraffe learns to play chess largely by self-play and derives its own rules based on the data [1]. Giraffe was implemented as a 7 class classification problem on a dataset of over 10,000 grandmaster level games. Four different implementations of Giraffe were explored covering two different architectures and the effects of regularization on the model performance.
The second algorithm implemented goes through an unsupervised learning phase to perform feature extraction followed by a supervised learning phase thus replicating David Eli’s DeepChess. DeepChess evaluates chess positions using a deep neural network without any a priori knowledge regarding the rules of chess. DeepChess is implemented as a siamese network of two disjoint deep belief networks connected to each other by fully connected layers [2]. This architecture was implemented as a binary classification problem on the same dataset as Giraffe and also on a larger dataset of LiChess games. Different implementations of DeepChess covering different training methodologies and parameter sets were executed. |
| first_indexed | 2024-10-01T05:16:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157572 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:16:27Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1575722022-05-20T05:47:28Z Deep learning for computer chess (part 1) Arora, Manav He Ying School of Computer Science and Engineering yhe@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence This report encompasses the implementation of two state-of-the-art machine learning algorithms for evaluating chess positions. The first algorithm makes use of artificial neural networks and manual feature representation thus closely following the implementation and architecture of Matthew Lai’s Giraffe. Giraffe learns to play chess largely by self-play and derives its own rules based on the data [1]. Giraffe was implemented as a 7 class classification problem on a dataset of over 10,000 grandmaster level games. Four different implementations of Giraffe were explored covering two different architectures and the effects of regularization on the model performance. The second algorithm implemented goes through an unsupervised learning phase to perform feature extraction followed by a supervised learning phase thus replicating David Eli’s DeepChess. DeepChess evaluates chess positions using a deep neural network without any a priori knowledge regarding the rules of chess. DeepChess is implemented as a siamese network of two disjoint deep belief networks connected to each other by fully connected layers [2]. This architecture was implemented as a binary classification problem on the same dataset as Giraffe and also on a larger dataset of LiChess games. Different implementations of DeepChess covering different training methodologies and parameter sets were executed. Bachelor of Engineering (Computer Science) 2022-05-20T05:47:28Z 2022-05-20T05:47:28Z 2022 Final Year Project (FYP) Arora, M. (2022). Deep learning for computer chess (part 1). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157572 https://hdl.handle.net/10356/157572 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Arora, Manav Deep learning for computer chess (part 1) |
| title | Deep learning for computer chess (part 1) |
| title_full | Deep learning for computer chess (part 1) |
| title_fullStr | Deep learning for computer chess (part 1) |
| title_full_unstemmed | Deep learning for computer chess (part 1) |
| title_short | Deep learning for computer chess (part 1) |
| title_sort | deep learning for computer chess part 1 |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/157572 |
| work_keys_str_mv | AT aroramanav deeplearningforcomputerchesspart1 |