Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/82104
Title: Automatic critical mechanic discovery using playtraces in video games
Authors: Cerny Green, Michael
Khalifa, Ahmed
Barros, Gabriella A. B.
Machado, Tiago
Togelius, Julian
Keywords: Computer games -- Design
Artificial intelligence
Machine learning
Human-computer interaction
Issue Date: 2020
Publisher: Association for Computing Machinery
Citation: Cerny Green, M., Khalifa, A., Barros, G. A. B., Machado, T., & Togelius, J. (2020). Automatic critical mechanic discovery using playtraces in video games. FDG '20: International Conference on the Foundations of Digital Games, Bugibba.
Abstract: We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied to several games within the General Video Game Artificial Intelligence (GVG-AI) framework. In a user study, human-identified mechanics are compared against system-identified critical mechanics to verify alignment between humans and the system. The results of the study demonstrate that the new method is able to match humans with higher consistency than baseline. Our system is further validated by comparing MCTS agents augmented with critical mechanics and vanilla MCTS agents on 4 games from GVG-AI. Our new playtrace method shows a significant performance improvement over the baseline for all 4 tested games. The proposed method also shows either matched or improved performance over the old method, demonstrating that playtrace information is responsible for more complete critical mechanic discovery.
URI: https://www.um.edu.mt/library/oar/handle/123456789/82104
Appears in Collections:Scholarly Works - InsDG

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