Advertisement

The experience-driven perspective

  • Noor ShakerEmail author
  • Julian Togelius
  • Georgios N. Yannakakis
Chapter
  • 2.4k Downloads
Part of the Computational Synthesis and Creative Systems book series (CSACS)

Abstract

Ultimately, content is generated for the player. But so far, our algorithms have not taken specific players into account. Creating computational models of a player’s behaviour, preferences, or skills is called player modelling. With a model of the player, we can create algorithms that create content specifically tailored to that player. The experience-driven perspective on procedural content generation provides a framework for content generation based on player modelling; one of the most important ways of doing this is to use a player model in the evaluation function for search-based PCG. This chapter discusses different ways of collecting and encoding data about the player, primarily player experience, and ways of modelling this data. It also gives examples of different ways in which such models can be used.

Keywords

Affective Computing Player Experience Grammatical Evolution Game Element Game Content 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bänziger, T., Tran, V., Scherer, K.R.: The Geneva Emotion Wheel: A tool for the verbal report of emotional reactions. In: Proceedings of the 2005 Conference of the International Society for Research on Emotion (2005)Google Scholar
  2. 2.
    Bianchi-Berthouze, N., Isbister, K.: Emotion and body-based games: Overview and opportunities. In: K. Karpouzis, G.N. Yannakakis (eds.) Emotion in Games: Theory and Praxis. Springer (2016)Google Scholar
  3. 3.
    Calleja, G.: In-Game: From Immersion to Incorporation. MIT Press (2011)Google Scholar
  4. 4.
    Conati, C.: Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence 16(7-8), 555–575 (2002)Google Scholar
  5. 5.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper & Row (1990)Google Scholar
  6. 6.
    Drachen, A., Thurau, C., Togelius, J., Yannakakis, G.N., Bauckhage, C.: Game data mining. In: M. Seif El-Nasr, A. Drachen, A. Canossa (eds.) Game Analytics, pp. 205–253. Springer (2013)Google Scholar
  7. 7.
    Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer (2011)Google Scholar
  8. 8.
    Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Cognitive Systems Research 5(4), 269–306 (2004)Google Scholar
  9. 9.
    Holmgård, C., Yannakakis, G.N., Karstoft, K.I., Andersen, H.S.: Stress detection for PTSD via the StartleMart game. In: Proceedings of the 5th International Conference on Affective Computing and Intelligent Interaction, pp. 523–528 (2013)Google Scholar
  10. 10.
    Hunicke, R., Chapman, V.: AI for dynamic difficulty adjustment in games. In: Proceedings of the AAAI Workshop on Challenges in Game Artificial Intelligence, pp. 91–96 (2004)Google Scholar
  11. 11.
    IJsselsteijn,W., de Kort, Y., Poels, K., Jurgelionis, A., Bellotti, F.: Characterising and measuring user experiences in digital games. In: Proceedings of the 2007 Conference on Advances in Computer Entertainment Technology (2007)Google Scholar
  12. 12.
    Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)Google Scholar
  13. 13.
    Martínez, H.P., Bengio, Y., Yannakakis, G.N.: Learning deep physiological models of affect. IEEE Computational Intelligence Magazine 8(2), 20–33 (2013)Google Scholar
  14. 14.
    Martínez, H.P., Garbarino, M., Yannakakis, G.N.: Generic physiological features as predictors of player experience. In: Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp. 267–276 (2011)Google Scholar
  15. 15.
    Martínez, H.P., Yannakakis, G.N.: Mining multimodal sequential patterns: A case study on affect detection. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 3–10 (2011)Google Scholar
  16. 16.
    Martínez, H.P., Yannakakis, G.N., Hallam, J.: Don’t classify ratings of affect; rank them! IEEE Transactions on Affective Computing 5(3), 314–326 (2014)Google Scholar
  17. 17.
    Metallinou, A., Narayanan, S.: Annotation and processing of continuous emotional attributes: Challenges and opportunities. In: Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition (2013)Google Scholar
  18. 18.
    Nijholt, A.: BCI for games: A ‘state of the art’ survey. In: Proceedings of the International Conference on Entertainment Computing, pp. 225–228 (2008)Google Scholar
  19. 19.
    Nintendo: (1985). Super Mario Bros., NintendoGoogle Scholar
  20. 20.
    Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press (1990)Google Scholar
  21. 21.
    Persson, M.: Infinite Mario Bros. URL http://www.mojang.com/notch/mario/
  22. 22.
    Savva, N., Scarinzi, A., Berthouze, N.: Continuous recognition of player’s affective body expression as dynamic quality of aesthetic experience. IEEE Transactions on Computational Intelligence and AI in Games 4(3), 199–212 (2012)Google Scholar
  23. 23.
    Shaker, N., Asteridadis, S., Karpouzis, K., Yannakakis, G.N.: Fusing visual and behavioral cues for modeling user experience in games. IEEE Transactions on Cybernetics 43(6), 1519–1531 (2013)Google Scholar
  24. 24.
    Shaker, N., Togelius, J., Yannakakis, G.N.: Towards automatic personalized content generation for platform games. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 63–68 (2010)Google Scholar
  25. 25.
    Shaker, N., Yannakakis, G., Togelius, J.: Crowdsourcing the aesthetics of platform games. IEEE Transactions on Computational Intelligence and AI in Games 5(3), 276–290 (2013)Google Scholar
  26. 26.
    Shaker, N., Yannakakis, G.N., Togelius, J., Nicolau, M., O’Neill, M.: Evolving personalized content for Super Mario Bros using grammatical evolution. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 75–80 (2012)Google Scholar
  27. 27.
    Sweetser, P., Wyeth, P.: Gameflow: A model for evaluating player enjoyment in games. ACM Computers in Entertainment 3(3) (2005)Google Scholar
  28. 28.
    Tognetti, S., Garbarino, M., Bonarini, A., Matteucci, M.: Modeling enjoyment preference from physiological responses in a car racing game. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games, pp. 321–328 (2010)Google Scholar
  29. 29.
    Yannakakis, G.N.: Preference learning for affective modeling. In: Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction (2009)Google Scholar
  30. 30.
    Yannakakis, G.N., Hallam, J.: Ranking vs. preference: A comparative study of self-reporting. In: Proceedings of the International Conference on Affective Computing and Intelligent Interaction, pp. 437–446 (2011)Google Scholar
  31. 31.
    Yannakakis, G.N., Martínez, H.P.: Grounding truth via ordinal annotation. In: Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction, pp. 574–580 (2015)Google Scholar
  32. 32.
    Yannakakis, G.N., Martínez, H.P.: Ratings are overrated! Frontiers in ICT 2, 13 (2015)Google Scholar
  33. 33.
    Yannakakis, G.N., Martínez, H.P., Garbarino, M.: Psychophysiology in games. In: K. Karpouzis, G.N. Yannakakis (eds.) Emotion in Games: Theory and Praxis. Springer (2016)Google Scholar
  34. 34.
    Yannakakis, G.N., Martínez, H.P., Jhala, A.: Towards affective camera control in games. User Modeling and User-Adapted Interaction 20(4), 313–340 (2010)Google Scholar
  35. 35.
    Yannakakis, G.N., Paiva, A.: Emotion in games. In: R.A. Calvo, S. D’Mello, J. Gratch, A. Kappas (eds.) Handbook of Affective Computing. Oxford University Press (2013)Google Scholar
  36. 36.
    Yannakakis, G.N., Spronck, P., Loiacono, D., Andre, E.: Player modeling. In: Dagstuhl Seminar on Artificial and Computational Intelligence in Games, pp. 45–59 (2013)Google Scholar
  37. 37.
    Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Transactions on Affective Computing 2(3), 147–161 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Noor Shaker
    • 1
    Email author
  • Julian Togelius
    • 2
  • Georgios N. Yannakakis
    • 3
  1. 1.Department of Architecture, Design and Media TechnologyAalborg University Copenhagen (AAU CPH)CopenhagenDenmark
  2. 2.Department of Computer Science and EngineeringNew York UniversityBrooklynUSA
  3. 3.Institute of Digital GamesUniversity of MaltaMsidaMalta

Personalised recommendations