Deep learning and computer chess (part 2)
Monte Carlo Tree Search (MCTS) is a probabilistic algorithm that has gained traction in recent years. MCTS uses lightweight random simulations to selectively grow a game tree and has experienced success in domains with vast search spaces, such as chess. This project explores the usage of the MCTS...
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2020
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sg-ntu-dr.10356-1449722020-12-07T04:30:51Z Deep learning and computer chess (part 2) Ngoh, Guang Wei He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering Monte Carlo Tree Search (MCTS) is a probabilistic algorithm that has gained traction in recent years. MCTS uses lightweight random simulations to selectively grow a game tree and has experienced success in domains with vast search spaces, such as chess. This project explores the usage of the MCTS algorithm in chess engines as well as the various ways MCTS can be improved beyond the base algorithm through the use of a static board state evaluation function. Methods such as early playout termination, implicit minimax backups, MCTS-Solver, as well as a few novel methods were implemented with their results being analyzed and discussed. The implemented methods proved to be a promising step in the right direction in developing a MCTS chess engine that can rival and potentially outperform chess engines that use deterministic algorithms with further development. Bachelor of Engineering (Computer Science) 2020-12-07T04:30:51Z 2020-12-07T04:30:51Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/144972 en SCSE19-0594 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Ngoh, Guang Wei Deep learning and computer chess (part 2) |
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Monte Carlo Tree Search (MCTS) is a probabilistic algorithm that has gained traction in recent years. MCTS uses lightweight random simulations to selectively grow a game tree and has experienced success in domains with vast search spaces, such as chess.
This project explores the usage of the MCTS algorithm in chess engines as well as the various ways MCTS can be improved beyond the base algorithm through the use of a static board state evaluation function. Methods such as early playout termination, implicit minimax backups, MCTS-Solver, as well as a few novel methods were implemented with their results being analyzed and discussed.
The implemented methods proved to be a promising step in the right direction in developing a MCTS chess engine that can rival and potentially outperform chess engines that use deterministic algorithms with further development. |
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He Ying |
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He Ying Ngoh, Guang Wei |
format |
Final Year Project |
author |
Ngoh, Guang Wei |
author_sort |
Ngoh, Guang Wei |
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) |
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Deep learning and computer chess (part 2) |
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Deep learning and computer chess (part 2) |
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deep learning and computer chess (part 2) |
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Nanyang Technological University |
publishDate |
2020 |
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https://hdl.handle.net/10356/144972 |
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1686109396895006720 |