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dc.contributor.authorHsueh, Chu-Hsuanen_US
dc.contributor.authorWu, I-Chenen_US
dc.contributor.authorTseng, Wen-Jieen_US
dc.contributor.authorYen, Shi-Jimen_US
dc.contributor.authorChen, Jr-Changen_US
dc.date.accessioned2017-04-21T06:55:21Z-
dc.date.available2017-04-21T06:55:21Z-
dc.date.issued2016-09-06en_US
dc.identifier.issn0304-3975en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.tcs.2016.06.025en_US
dc.identifier.urihttp://hdl.handle.net/11536/134241-
dc.description.abstractMonte Carlo tree search (MCTS) has been successfully applied to many games recently. Since then, many techniques are used to improve the strength of MCTS-based programs. This paper investigates four recent techniques: early playout terminations, implicit minimax backups, quality-based rewards and progressive bias. The strength improvements are analyzed by incorporating the techniques into an MCTS-based program, named DARKKNIGHT, for Chinese Dark Chess. Experimental results showed that the win rates against the original DARKKNIGHT were 60.75%, 71.85%, 59.00%, and 82.10%, respectively for incorporating the four techniques. The results indicated that the improvement by progressive bias was most significant. By incorporating all together, a better win rate of 84.75% was obtained. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectMonte Carlo tree searchen_US
dc.subjectChinese dark chessen_US
dc.subjectEarly playout terminationsen_US
dc.subjectImplicit minimax backupsen_US
dc.subjectQuality-based rewardsen_US
dc.subjectProgressive biasen_US
dc.titleAn analysis for strength improvement of an MCTS-based program playing Chinese dark chessen_US
dc.identifier.doi10.1016/j.tcs.2016.06.025en_US
dc.identifier.journalTHEORETICAL COMPUTER SCIENCEen_US
dc.citation.volume644en_US
dc.citation.spage63en_US
dc.citation.epage75en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000383822700006en_US
Appears in Collections:Articles