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http://dx.doi.org/10.5859/KAIS.2016.25.4.23

Enhanced MCTS Algorithm for Generating AI Agents in General Video Games  

Oh, Pyeong (한림대학교 인터랙션 디자인학)
Kim, Ji-Min (한림대학교 인터랙션 디자인학)
Kim, Sun-Jeong (한림대학교 융합소프트웨어학과)
Hong, Seokmin (한림대학교 광고홍보학과)
Publication Information
The Journal of Information Systems / v.25, no.4, 2016 , pp. 23-36 More about this Journal
Abstract
Purpose Recently, many researchers have paid much attention to the Artificial Intelligence fields of GVGP, PCG. The paper suggests that the improved MCTS algorithm to apply for the framework can generate better AI agent. Design/methodology/approach As noted, the MCTS generate magnificent performance without an advanced training and in turn, fit applying to the field of GVGP which does not need prior knowledge. The improved and modified MCTS shows that the survival rate is increased interestingly and the search can be done in a significant way. The study was done with 2 different sets. Findings The results showed that the 10 training set which was not given any prior knowledge and the other training set which played a role as validation set generated better performance than the existed MCTS algorithm. Besed upon the results, the further study was suggested.
Keywords
Monte-Carlo Tree Searching; General Game Playing; GVG-AI (General Video Games Artificial Intelligence;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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