Job-Level Alpha-Beta Search
Jr-Chang Chen, I‐Chen Wu, Wen-Jie Tseng, Bo-Han Lin, Chia‐Hui Chang · IEEE Transactions on Computational Intelligence and AI in Games · 2014
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.