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

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

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.

Original language	English
Article number	6785996
Pages (from-to)	28-38
Number of pages	11
Journal	IEEE Transactions on Computational Intelligence and AI in Games
Volume	7
Issue number	1
DOIs	https://doi.org/10.1109/TCIAIG.2014.2316314
State	Published - 1 Mar 2015

Keywords

Alpha-beta search
chinese chess
game tree search
job-level computing
opening book

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10.1109/TCIAIG.2014.2316314

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title = "Job-level alpha-beta search",

abstract = "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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AU - Lin, Bo Han

AU - Chang, Chia Hui

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AB - 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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Job-level alpha-beta search

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Keywords

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