Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCIAIG.2014.2316314 | IEEE Trans. Comput. Intellig. and AI in Games |
Keywords | Field | DocType |
best-first search version,jl-abs algorithm,game tree search,alpha-beta search,job-level computing,chinese chess,search problems,job-level alpha-beta search,computer game application,opening book,computer games,algorithm design and analysis,computer science,alpha beta search,games,parallel processing | Algorithm design,Computer science,Parallel processing,Beam search,Theoretical computer science,Game tree search,Artificial intelligence,Best-first search,Alpha–beta pruning | Journal |
Volume | Issue | ISSN |
7 | 1 | 1943-068X |
Citations | PageRank | References |
6 | 1.14 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jr-Chang Chen | 1 | 42 | 15.19 |
| I-Chen Wu | 2 | 208 | 55.03 |
| Wen-Jie Tseng | 3 | 26 | 8.89 |
| Bo-Han Lin | 4 | 6 | 1.14 |
| Chia-Hui Chang | 5 | 6 | 1.48 |