TY - GEN
T1 - Automatic Critical Mechanic Discovery Using Playtraces in Video Games
AU - Green, Michael Cerny
AU - Khalifa, Ahmed
AU - Barros, Gabriella A.B.
AU - MacHado, Tiago
AU - Togelius, Julian
N1 - Funding Information:
Michael Cerny Green acknowledges the financial support of the GAANN program. Ahmed Khalifa acknowledges the financial support from NSF grant (Award number 1717324 - “RI: Small: General Intelligence through Algorithm Invention and Selection.”). Tiago Machado acknowledges the finacial support from CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico under the Science without Borders scholarship 202859/2015-0.
Funding Information:
Michael Cerny Green acknowledges the financial support of the GAANN program. Ahmed Khalifa acknowledges the financial support from NSF grant (Award number 1717324-"RI: Small: General Intelligence through Algorithm Invention and Selection."). Tiago Machado acknowledges the finacial support from CNPq-Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico under the Science without Borders scholarship 202859/2015-0.
Publisher Copyright:
© 2020 ACM.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - 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.
AB - 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.
KW - game playing
KW - monte carlo tree search
KW - planning
KW - tutorial generation
UR - http://www.scopus.com/inward/record.url?scp=85092274312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092274312&partnerID=8YFLogxK
U2 - 10.1145/3402942.3402955
DO - 10.1145/3402942.3402955
M3 - Conference contribution
AN - SCOPUS:85092274312
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 15th International Conference on the Foundations of Digital Games, FDG 2020
A2 - Yannakakis, Georgios N.
A2 - Liapis, Antonios
A2 - Penny, Kyburz
A2 - Volz, Vanessa
A2 - Khosmood, Foaad
A2 - Lopes, Phil
PB - Association for Computing Machinery
T2 - 15th International Conference on the Foundations of Digital Games, FDG 2020
Y2 - 15 September 2020 through 18 September 2020
ER -