Job-level alpha-beta search

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 languageEnglish
Article number6785996
Pages (from-to)28-38
Number of pages11
JournalIEEE Transactions on Computational Intelligence and AI in Games
Issue number1
StatePublished - 1 Mar 2015


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


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