{"id":{"repo_id":"maryland","oai_identifier":"oai:drum.lib.umd.edu:1903/20449"},"canonical_url":"https://search.dev.ndltd.org/etd/maryland/oai:drum.lib.umd.edu:1903/20449","repository":{"repo_id":"maryland","name":"University of Maryland","base_url":"https://api.drum.lib.umd.edu/server/oai/request"},"display":{"title":"MONTE CARLO TREE SEARCH AND MINIMAX COMBINATION – APPLICATION OF SOLVING PROBLEMS IN THE GAME OF GO","abstract":"Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with alpha-beta pruning in games with high branching factors such as Go. However, minimax search with alpha-beta pruning still surpasses MCTS in domains like Chess. Studies show that MCTS does not detect shallow traps, where opponents can win within a few moves, as well as minimax search. Thus, minimax search performs better than MCTS in games like Chess, which can end instantly (king is captured). A combination of MCTS and minimax algorithm is proposed in this thesis to see the effectiveness of detecting shallow traps in Go problems.","abstract_html":"Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with alpha-beta pruning in games with high branching factors such as Go. However, minimax search with alpha-beta pruning still surpasses MCTS in domains like Chess. Studies show that MCTS does not detect shallow traps, where opponents can win within a few moves, as well as minimax search. Thus, minimax search performs better than MCTS in games like Chess, which can end instantly (king is captured). A combination of MCTS and minimax algorithm is proposed in this thesis to see the effectiveness of detecting shallow traps in Go problems.","abstract_has_math":false,"creators":["Lin, Jonathan Fun"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Systems Engineering","school":null,"contributors":[],"advisors":["Fu, Michael"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T03:02:17Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.13016/M2VD6P64Q"],"render_values":[{"text":"https://doi.org/10.13016/M2VD6P64Q","href":"https://doi.org/10.13016/M2VD6P64Q","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1903/20449","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fu, Michael"]},{"key":"dc:contributor.department","label":"Department","values":["Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Lin, Jonathan Fun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-25T06:36:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-25T06:36:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.13016/M2VD6P64Q"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1903/20449"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with alpha-beta pruning in games with high branching factors such as Go. However, minimax search with alpha-beta pruning still surpasses MCTS in domains like Chess. Studies show that MCTS does not detect shallow traps, where opponents can win within a few moves, as well as minimax search. Thus, minimax search performs better than MCTS in games like Chess, which can end instantly (king is captured). A combination of MCTS and minimax algorithm is proposed in this thesis to see the effectiveness of detecting shallow traps in Go problems."]},{"key":"dc:title","label":"Title","values":["MONTE CARLO TREE SEARCH AND MINIMAX COMBINATION – APPLICATION OF SOLVING PROBLEMS IN THE GAME OF GO"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fu, Michael"],"dc:contributor.department":["Systems Engineering"],"dc:creator":["Lin, Jonathan Fun"],"dc:date.accessioned":["2018-01-25T06:36:18Z"],"dc:date.available":["2018-01-25T06:36:18Z"],"dc:date.issued":["2017"],"dc:description.abstract":["Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with alpha-beta pruning in games with high branching factors such as Go. However, minimax search with alpha-beta pruning still surpasses MCTS in domains like Chess. Studies show that MCTS does not detect shallow traps, where opponents can win within a few moves, as well as minimax search. Thus, minimax search performs better than MCTS in games like Chess, which can end instantly (king is captured). A combination of MCTS and minimax algorithm is proposed in this thesis to see the effectiveness of detecting shallow traps in Go problems."],"dc:identifier":["https://doi.org/10.13016/M2VD6P64Q"],"dc:identifier.uri":["http://hdl.handle.net/1903/20449"],"dc:language.iso":["en"],"dc:title":["MONTE CARLO TREE SEARCH AND MINIMAX COMBINATION – APPLICATION OF SOLVING PROBLEMS IN THE GAME OF GO"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:02:17Z"}