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dc.contributor.authorChen, Jr-Changen_US
dc.contributor.authorWu, I-Chenen_US
dc.contributor.authorTseng, Wen-Jieen_US
dc.contributor.authorLin, Bo-Hanen_US
dc.contributor.authorChang, Chia-Huien_US
dc.date.accessioned2015-07-21T08:29:21Z-
dc.date.available2015-07-21T08:29:21Z-
dc.date.issued2015-03-01en_US
dc.identifier.issn1943-068Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/TCIAIG.2014.2316314en_US
dc.identifier.urihttp://hdl.handle.net/11536/124541-
dc.description.abstractAn approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese chess. The experimental results demonstrated that JL-ABS reached a speed-up of 10.69 when using 16 workers in the JL system.en_US
dc.language.isoen_USen_US
dc.subjectAlpha-beta searchen_US
dc.subjectchinese chessen_US
dc.subjectgame tree searchen_US
dc.subjectjob-level computingen_US
dc.subjectopening booken_US
dc.titleJob-Level Alpha-Beta Searchen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TCIAIG.2014.2316314en_US
dc.identifier.journalIEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMESen_US
dc.citation.spage28en_US
dc.citation.epage38en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000351542300004en_US
dc.citation.woscount0en_US
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