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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162926 |
| _version_ | 1826117156560961536 |
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| author | Seah, Yu Liang |
| author2 | He Ying |
| author_facet | He Ying Seah, Yu Liang |
| author_sort | Seah, Yu Liang |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T04:23:04Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162926 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:23:04Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Computer science and engineering Seah, Yu Liang Deep learning and computer chess (part 2) |
| title | 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_short | Deep learning and computer chess (part 2) |
| title_sort | deep learning and computer chess part 2 |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/162926 |
| work_keys_str_mv | AT seahyuliang deeplearningandcomputerchesspart2 |