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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Calleja, G.: In-Game: From Immersion to Incorporation. MIT Press (2011)
Conati, C.: Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence 16(7-8), 555–575 (2002)
Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper & Row (1990)
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)
Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer (2011)
Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Cognitive Systems Research 5(4), 269–306 (2004)
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)
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)
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)
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)
MartÃnez, H.P., Bengio, Y., Yannakakis, G.N.: Learning deep physiological models of affect. IEEE Computational Intelligence Magazine 8(2), 20–33 (2013)
MartÃnez, 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)
MartÃnez, 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)
MartÃnez, 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)
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)
Nijholt, A.: BCI for games: A ‘state of the art’ survey. In: Proceedings of the International Conference on Entertainment Computing, pp. 225–228 (2008)
Nintendo: (1985). Super Mario Bros., Nintendo
Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press (1990)
Persson, M.: Infinite Mario Bros. URL http://www.mojang.com/notch/mario/
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)
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)
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)
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)
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)
Sweetser, P., Wyeth, P.: Gameflow: A model for evaluating player enjoyment in games. ACM Computers in Entertainment 3(3) (2005)
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)
Yannakakis, G.N.: Preference learning for affective modeling. In: Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction (2009)
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)
Yannakakis, G.N., MartÃnez, H.P.: Grounding truth via ordinal annotation. In: Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction, pp. 574–580 (2015)
Yannakakis, G.N., MartÃnez, H.P.: Ratings are overrated! Frontiers in ICT 2, 13 (2015)
Yannakakis, G.N., MartÃnez, H.P., Garbarino, M.: Psychophysiology in games. In: K. Karpouzis, G.N. Yannakakis (eds.) Emotion in Games: Theory and Praxis. Springer (2016)
Yannakakis, G.N., MartÃnez, H.P., Jhala, A.: Towards affective camera control in games. User Modeling and User-Adapted Interaction 20(4), 313–340 (2010)
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)
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)
Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Transactions on Affective Computing 2(3), 147–161 (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Shaker, N., Togelius, J., Yannakakis, G.N. (2016). The experience-driven perspective. In: Procedural Content Generation in Games. Computational Synthesis and Creative Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-42716-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-42716-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42714-0
Online ISBN: 978-3-319-42716-4
eBook Packages: Computer ScienceComputer Science (R0)