Procedural Personas as Critics for Dungeon Generation

  • Antonios Liapis
  • Christoffer Holmgård
  • Georgios N. Yannakakis
  • Julian Togelius
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)

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.

Keywords

Level Feature Artificial Agent Strategy Game Human Player Game Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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).

References

  1. 1.
    van der Linden, R., Lopes, R., Bidarra, R.: Procedural generation of dungeons. IEEE Trans. Comput. Intell. AI Games 6(1), 78–89 (2013)CrossRefGoogle Scholar
  2. 2.
    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) CrossRefGoogle Scholar
  3. 3.
    Dormans, J.: Adventures in level design: generating missions and spaces for action adventure games. In: Workshop on Procedural Content Generation in Games (2010)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica J. Econometric Soc. 47, 263–291 (1979)CrossRefMATHGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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) CrossRefGoogle Scholar
  14. 14.
    Björk, S., Holopainen, J.: Patterns in Game Design. Charles River Media, Rockland (2004)Google Scholar
  15. 15.
    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) CrossRefGoogle Scholar
  16. 16.
    Horswill, I., Foged, L.: Fast procedural level population with playability constraints. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (2012)Google Scholar
  17. 17.
    Michalewicz, Z., Dasgupta, D., Le Riche, R., Schoenauer, M.: Evolutionary algorithms for constrained engineering problems. Comput. Ind. Eng. J. 30, 851–870 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Antonios Liapis
    • 1
  • Christoffer Holmgård
    • 2
  • Georgios N. Yannakakis
    • 1
    • 2
  • Julian Togelius
    • 2
  1. 1.Institute of Digital GamesUniversity of MaltaMsidaMalta
  2. 2.Center for Computer Games ResearchIT University of CopenhagenCopenhagenDenmark

Personalised recommendations