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.
Mario Level Generation From Mechanics Using Scene Stitching
Video game tutorials allow players to gain mastery over game skills and mechanics. To hone players’ skills, it is beneficial from practicing in environments that promote individ- ual player skill sets. However, automatically generating environ- ments which are mechanically similar to one-another is a non- trivial problem. This paper presents a level generation method for Super Mario by stitching together pre-generated “scenes” that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications. Given a sequence of mechanics, the proposed system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The proposed system outputs levels that can be beaten using a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare the proposed system to a greedy method that selects scenes that maximize the number of matched mechanics. Unlike the greedy approach, the proposed system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process.
- Award ID(s):
- 1717324
- Publication Date:
- NSF-PAR ID:
- 10231877
- Journal Name:
- IEEE Conference on Games
- Page Range or eLocation-ID:
- 49 to 56
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/CoG47356.2020.9231692
