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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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 (\(F_{MK}\), \(F_{TC}\), \(D_{MK}\), \(D_{TC}\)).
References
van der Linden, R., Lopes, R., Bidarra, R.: Procedural generation of dungeons. IEEE Trans. Comput. Intell. AI Games 6(1), 78–89 (2013)
Roden, T., Parberry, I.: From artistry to automation: a structured methodology for procedural content creation. In: Rauterberg, M. (ed.) ICEC 2004. LNCS, vol. 3166, pp. 151–156. Springer, Heidelberg (2004)
Dormans, J.: Adventures in level design: generating missions and spaces for action adventure games. In: Workshop on Procedural Content Generation in Games (2010)
Hartsook, K., Zook, A., Das, S., Riedl, M.: Toward supporting stories with procedurally generated game worlds. In: Proceedings of the IEEE Conference on Computational Intelligence and Games, pp. 297–304 (2011)
Togelius, J., Yannakakis, G.N., Stanley, K.O., Browne, C.: Search-based procedural content generation: a taxonomy and survey. IEEE Trans. Comput. Intell. AI Games 3(3), 172–186 (2011)
Holmgård, C., Liapis, A., Togelius, J., Yannakakis, G.N.: Evolving personas for player decision modeling. In: Proceedings of the IEEE Conference on Computational Intelligence and Games (2014)
Kimbrough, S.O., Koehler, G.J., Lu, M., Wood, D.H.: On a feasible-infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Eur. J. Oper. Res. 190(2), 310–327 (2008)
Holmgård, C., Liapis, A., Togelius, J., Yannakakis, G.N.: Generative agents for player decision modeling in games. In: Poster Proceedings of the 9th Conference on the Foundations of Digital Games (2014)
Liapis, A., Yannakakis, G., Togelius, J.: Sentient sketchbook: computer-aided game level authoring. In: Proceedings of the ACM Conference on Foundations of Digital Games (2013)
Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica J. Econometric Soc. 47, 263–291 (1979)
Gibson, J.J.: The theory of affordances. In: Shaw, R.E., Bransford, J. (eds.) Perceiving, Acting, and Knowing, pp. 67–82. Lawrence Erlbaum Associates, Hillsdale (1977)
Liapis, A., Yannakakis, G.N., Togelius, J.: Towards a generic method of evaluating game levels. In: Proceedings of the AAAI Artificial Intelligence for Interactive Digital Entertainment Conference (2013)
Liapis, A., Yannakakis, G.N., Togelius, J.: Generating map sketches for strategy games. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 264–273. Springer, Heidelberg (2013)
Björk, S., Holopainen, J.: Patterns in Game Design. Charles River Media, Rockland (2004)
Holmgård, C., Liapis, A., Togelius, J., Yannakakis, G.N.: Personas versus clones for player decision modeling. In: Pisan, Y., Sgouros, N.M., Marsh, T. (eds.) ICEC 2014. LNCS, vol. 8770, pp. 159–166. Springer, Heidelberg (2014)
Horswill, I., Foged, L.: Fast procedural level population with playability constraints. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (2012)
Michalewicz, Z., Dasgupta, D., Le Riche, R., Schoenauer, M.: Evolutionary algorithms for constrained engineering problems. Comput. Ind. Eng. J. 30, 851–870 (1996)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-16549-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16548-6
Online ISBN: 978-3-319-16549-3
eBook Packages: Computer ScienceComputer Science (R0)