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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Main Author: Ngoh, Guang Wei
Other Authors: He Ying
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/144972
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Ngoh, Guang Wei
Deep learning and computer chess (part 2)
description 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.
author2 He Ying
author_facet 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)
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 2020
url https://hdl.handle.net/10356/144972
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