Job-level alpha-beta search

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

doi:10.1109/TCIAIG.2014.2316314

Job-level alpha-beta search

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

資訊工程學系

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.

原文	???core.languages.en_GB???
文章編號	6785996
頁（從 - 到）	28-38
頁數	11
期刊	IEEE Transactions on Computational Intelligence and AI in Games
卷	7
發行號	1
DOIs	https://doi.org/10.1109/TCIAIG.2014.2316314
出版狀態	已出版 - 1 3月 2015

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10.1109/TCIAIG.2014.2316314

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Job-level alpha-beta search

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