Name
Title | ||
|---|---|---|
An analysis for strength improvement of an MCTS-based program playing Chinese dark chess. |
Abstract | ||
|---|---|---|
Monte Carlo tree search (MCTS) has been successfully applied to many games recently. Since then, many techniques are used to improve the strength of MCTS-based programs. This paper investigates four recent techniques: early playout terminations, implicit minimax backups, quality-based rewards and progressive bias. The strength improvements are analyzed by incorporating the techniques into an MCTS-based program, named DarkKnight, for Chinese Dark Chess. Experimental results showed that the win rates against the original DarkKnight were 60.75%, 71.85%, 59.00%, and 82.10%, respectively for incorporating the four techniques. The results indicated that the improvement by progressive bias was most significant. By incorporating all together, a better win rate of 84.75% was obtained. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.tcs.2016.06.025 | Theor. Comput. Sci. |
Keywords | Field | DocType |
Monte Carlo tree search,Chinese dark chess,Early playout terminations,Implicit minimax backups,Quality-based rewards,Progressive bias | Minimax,Monte Carlo tree search,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
644 | C | 0304-3975 |
Citations | PageRank | References |
2 | 0.39 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chu-Hsuan Hsueh | 1 | 11 | 4.21 |
| I-Chen Wu | 2 | 208 | 55.03 |
| Wen-Jie Tseng | 3 | 26 | 8.89 |
| Shi-jim Yen | 4 | 134 | 27.99 |
| Jr-Chang Chen | 5 | 42 | 15.19 |