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Paper detail

Mario Level Generation From Mechanics Using Scene Stitching

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, our system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The system outputs levels that have a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare our system to a greedy method that selects scenes that maximize the target mechanics. Our system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process compared to the greedy approach.

preprint2020arXivOpen access
Michael Cerny GreenLuvneesh MugraiAhmed KhalifaJulian Togelius
Neural and Evolutionary ComputingArtificial Intelligence
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Michael Cerny GreenLuvneesh MugraiAhmed KhalifaJulian Togelius

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