Name
Abstract | ||
|---|---|---|
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 individual player skill sets. However, automatically generating environments 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CoG47356.2020.9231692 | 2020 IEEE Conference on Games (CoG) |
Keywords | DocType | ISSN |
Super Mario Bros,Feasible-Infeasible 2-Population,Evolutionary Algorithms,Stitching,Design Patterns,PCG,Experience Driven PCG | Conference | 2325-4270 |
ISBN | Citations | PageRank |
978-1-7281-4534-1 | 1 | 0.36 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Cerny Green | 1 | 31 | 7.17 |
| Mugrai Luvneesh | 2 | 1 | 0.36 |
| Ahmed Aziz Khalifa | 3 | 69 | 12.04 |
| Julian Togelius | 4 | 2765 | 219.94 |