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MCTS/EA hybrid GVGAI players and game difficulty estimation

Horn, H and Volz, V and Perez-Liebana, D and Preuss, M (2016) MCTS/EA hybrid GVGAI players and game difficulty estimation. In: UNSPECIFIED, ? - ?.

MCTSEAHybridGVG.pdf - Accepted Version

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In the General Video Game Playing competitions of the last years, Monte-Carlo tree search as well as Evolutionary Algorithm based controllers have been successful. However, both approaches have certain weaknesses, suggesting that certain hybrids could outperform both. We envision and experimentally compare several types of hybrids of two basic approaches, as well as some possible extensions. In order to achieve a better understanding of the games in the competition and the strength and weaknesses of different controllers, we also propose and apply a novel game difficulty estimation scheme based on several observable game characteristics.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: IEEE Conference on Computatonal Intelligence and Games, CIG
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Diego Perez Liebana
Date Deposited: 22 Feb 2017 15:23
Last Modified: 30 Mar 2021 19:15

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