Deep learning and computer chess (part 2)
A machine would need more than 10^90 years to make the first chess move using brute force method. To address this problem, various strategies based on minimax algorithms and deep learning advancements have surfaced throughout time. One innovation that is able to address this problem is giraffe ar...
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2022
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sg-ntu-dr.10356-1629262022-11-14T04:27:49Z Deep learning and computer chess (part 2) Seah, Yu Liang He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering A machine would need more than 10^90 years to make the first chess move using brute force method. To address this problem, various strategies based on minimax algorithms and deep learning advancements have surfaced throughout time. One innovation that is able to address this problem is giraffe architecture. The data set was carefully chosen from chess matches between different grandmasters from around the world. To test the idea and see how the models react, various Giraffe models with different parameter settings are created. Bachelor of Engineering (Computer Science) 2022-11-14T04:27:48Z 2022-11-14T04:27:48Z 2022 Final Year Project (FYP) Seah, Y. L. (2022). Deep learning and computer chess (part 2). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162926 https://hdl.handle.net/10356/162926 en SCSE21-0733 application/pdf Nanyang Technological University |
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Nanyang Technological University |
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NTU Library |
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Asia |
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Singapore Singapore |
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Engineering::Computer science and engineering |
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Engineering::Computer science and engineering Seah, Yu Liang Deep learning and computer chess (part 2) |
| description |
A machine would need more than 10^90 years to make the first chess move using brute force
method. To address this problem, various strategies based on minimax algorithms and deep
learning advancements have surfaced throughout time. One innovation that is able to address
this problem is giraffe architecture. The data set was carefully chosen from chess matches
between different grandmasters from around the world. To test the idea and see how the models
react, various Giraffe models with different parameter settings are created. |
| author2 |
He Ying |
| author_facet |
He Ying Seah, Yu Liang |
| format |
Final Year Project |
| author |
Seah, Yu Liang |
| author_sort |
Seah, Yu Liang |
| title |
Deep learning and computer chess (part 2) |
| title_short |
Deep learning and computer chess (part 2) |
| title_full |
Deep learning and computer chess (part 2) |
| title_fullStr |
Deep learning and computer chess (part 2) |
| title_full_unstemmed |
Deep learning and computer chess (part 2) |
| title_sort |
deep learning and computer chess (part 2) |
| publisher |
Nanyang Technological University |
| publishDate |
2022 |
| url |
https://hdl.handle.net/10356/162926 |
| _version_ |
1751548510711316480 |