Job-level alpha-beta search

Jr Chang Chen, I. Chen Wu, Wen Jie Tseng, Bo Han Lin, Chia Hui Chang

研究成果: 雜誌貢獻期刊論文同行評審

8 引文 斯高帕斯(Scopus)

摘要

An 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.

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文章編號6785996
頁(從 - 到)28-38
頁數11
期刊IEEE Transactions on Computational Intelligence and AI in Games
7
發行號1
DOIs
出版狀態已出版 - 1 3月 2015

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