Abstract
This paper introduces a constrained optimization method which uses procedural personas to evaluate the playability and quality of evolved dungeon levels. Procedural personas represent archetypical player behaviors, and their controllers have been evolved to maximize a specific utility which drives their decisions. A “baseline” persona evaluates whether a level is playable by testing if it can survive in a worst-case scenario of the playthrough. On the other hand, a Monster Killer persona or a Treasure Collector persona evaluates playable levels based on how many monsters it can kill or how many treasures it can collect, respectively. Results show that the implemented two-population genetic algorithm discovers playable levels quickly and reliably, while the different personas affect the layout, difficulty level and tactical depth of the generated dungeons.
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Notes
- 1.
The fitness function of all personas’ controllers included a penalty for taking extraneous actions. Since this penalty was a control mechanism to avoid playthroughs taking too long rather than an explicit utility, it is omitted for the purposes of level evaluation.
- 2.
Since the infeasible fitness (\(d_{inf}\)) is the same for all experiments, discovery of the first feasible individual is calculated based on all four sets of experiments (\(F_{MK}\), \(F_{TC}\), \(D_{MK}\), \(D_{TC}\)).
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Acknowledgements
The research was supported, in part, by the FP7 ICT project C2Learn (project no: 318480) and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).
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Liapis, A., Holmgård, C., Yannakakis, G.N., Togelius, J. (2015). Procedural Personas as Critics for Dungeon Generation. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_27
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