Institute of Electrical and Electronics Engineers Inc.;IEEE
Abstract:
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. 其他題名： TCIAIG 出版者： IEEE 出版日期： 2015-03 出處： IEEE transactions on computational intelligence and AI in games., 2015-03, Vol.7 (1), p.28-38 資源來源： IEEE Xplore 識別號： ISSN: 1943-068X 識別號： EISSN: 1943-0698 識別號： DOI: 10.1109/TCIAIG.2014.2316314 識別號： CODEN: TCIARR
