Skip to main content
SpringerLink
Log in

Real-time rule-based classification of player types in computer games

  • Original Paper
  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a rule- performance metric, and experimentally validated. We achieve ~70% accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Acuña D.E., Parada V.: People efficiently explore the solution space of the computationally intractable traveling salesman problem to find near-optimal tours. PloS one 5(7), e11685 (2010)

    Article  Google Scholar 

  • Anscombe F.J.: Graphs in statistical analysis. Am. Stat. 27(1), 17–21 (1973)

    Google Scholar 

  • Anyanwu M., Shiva S.: Comparative analysis of serial decision tree classification algorithms. Int. J. Comput. Sci. Secur. 3(3), 230–240 (2009)

    Google Scholar 

  • Bartle, R.: Hearts, clubs, diamonds, spades: players who suit MUDs. J. Virtual Environ. 1(1) (1996)

  • Bateman C., Boon R.: 21st Century Game Design, vol. 1. Charles River Media, Londan (2005)

    Google Scholar 

  • Bateman, C., Lowenhaupt, R., Nacke, L.: Player typology in theory and practice. In: Proceedings of DiGRA: Think Design Play 2011. Utrecht, The Netherlands (2011)

  • Baumgarten, R.: Towards automatic player behaviour characterisation using multiclass linear discriminant analysis. In: Proceedings of the Artificial Intelligence and Simulation of Behaviour Symposium: AI and Games, March 2010. De Montfort University, Leicester, UK (2010)

  • Beal, C., Beck, J., Westbrook, D., Atkin, M., Cohen, P.: Intelligent modeling of the user in interactive entertainment. In: Forbus, K., El-Nasr, M.S. (eds.) Artificial Intelligence and Interactive Entertainment II. Papers from the AAAI Spring Symposium, Stanford, CA (2002)

  • Black M., Hickey R.J.: Maintaining the performance of a learned classifier under concept drift. Intell. Data Anal. 3(6), 453–474 (1999)

    Article  Google Scholar 

  • Bowling M., Fürnkranz J., Graepel T., Musick R.: Machine learning and games. Mach. Learn. 63(3), 211–215 (2006)

    Article  Google Scholar 

  • Breiman L.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)

    MATH  Google Scholar 

  • Caillois R.: Man, Play, and Games. Free Press of Glencoe, New York (1961)

    Google Scholar 

  • Chaperot, B., Fyfe, C.: Improving artificial intelligence in a motocross game. In: Proceedings of IEEE Symposium on Computational Intelligence and Games, pp. 181–186 (2006)

  • Chen G., Liu H., Yu L., Wei Q., Zhang X.: A new approach to classification based on association rule mining. Decis. Support Syst. 42(2), 674–689 (2006)

    Article  Google Scholar 

  • Cowley B., Moutinho J., Bateman C., Oliveira A.: Learning principles and interaction design for ‘green my place’: a massively multiplayer serious game. Entertain. Comput. 2(2), 10 (2011)

    Google Scholar 

  • Cowley, B., Charles, D., Black, M., Hickey, R.: Developing features of game metrics to describe videogame-player behaviour (in preparation, a)

  • Cowley, B., Kosunen, I., Kivikangas, J. M., Järvelä, S., Lankoski, P., Kemppainen, J.: Machine learning and play patterns: a pilot study for the PPAX framework. J. Simul. Gaming (in preparation, b)

  • Curtis, P.: Mudding: Social Phenomena in Text-Based Virtual Realities. Intertrek (3), 26–34 (1992). http://w2.eff.org/Net_culture/MOO_MUD_IRC/curtis_mudding.article

  • Drachen, A., Canossa, A.,Yannakakis, G.N.: Player modeling using self-organization in Tomb Raider: Underworld. In: Proceedings of the 5th International Conference on Computational Intelligence and Games, pp. 1–8. IEEE Press, Milano (2009)

  • Fu D., Houlette R., Rabin S.: Constructing a Decision Tree Based on Past Experience AI Game Programming Wisdom 2, pp. 567–577. Charles River Media, Hingham (2004)

    Google Scholar 

  • Fürnkranz, J.: Machine learning and game-playing. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 633–637. Springer, New York (2010)

  • Galway L., Charles D., Black M.: Machine learning in digital games: a survey. Artif. Intell. Rev. 29(2), 123–161 (2008)

    Article  Google Scholar 

  • Geisler, B.: An empirical study of machine learning algorithms applied to modelling player behaviour in a first person shooter video game. M.S. thesis, Department of Computer Sciences, University of Wisconsin, Madison (2002)

  • Grimes, S.: Mining the game: when marketing and gaming meet they do a lot more than advertise. Escapist Magazine, 3, 27 February 2007

  • Herbrich R., Minka T., Graepel T.: TrueSkill: a Bayesian skill rating system. Adv. Neural Inf. Process. Syst. 19, 569–576 (2007)

    Google Scholar 

  • Huizinga J.: Homo Ludens: A Study of the Play-Element of Culture. Routledge, London (1949)

    Google Scholar 

  • Johansson, U., König, R., Niklasson, L.: The truth is in there—rule extraction from opaque models using genetic programming. In: Proceedings of the 17 th International Florida Artificial Intelligence Research Society Conference, FL, p. 113 (2004)

  • Johansson, U., Sonstrod, C., Niklasson, L.: Explaining winning Poker—a data mining approach. In: Proceedings of the 5th International Conference on Machine Learning and Applications, pp. 129–134. IEEE Computer Society, Washington (2006)

  • Jones L.: Winning Low-Limit Hold’em. ConJelCo, Pittsburgh (2000)

    Google Scholar 

  • Jung C.G.: Psychological Types (Collected Works of C.G. Jung, vol. 6). Princeton University Press, Princeton (1971)

    Google Scholar 

  • Katz-Haas, R.: User-centered design and web development. Usability Interface 5(1), 12–13 (1998)

    Google Scholar 

  • Kaukoranta, J., Smed, H.: Role of pattern recognition in computer games. In: Sing, L.W., Man, W.H., Wai, W. (eds.) Proceedings of the 2nd International Conference on Application and Development of Computer Games, pp. 189–194. Hong Kong SAR, China (2003)

  • Kaukoranta T., Smed J., Hakonen H., Rabin S.: Understanding Pattern Recognition Methods. AI Game Programming Wisdom 2, pp. 579–589. Charles River Media, Hingham (2004)

    Google Scholar 

  • Keirsey D., Bates M.M.: Please Understand Me: Character & Temperament Types. Distributed by Prometheus Nemesis Book Co, Del Mar (1984)

    Google Scholar 

  • Kennerly, D.: Better game design through data mining. The art and business of making games, Gamasutra (2003). http://gamasutra.com/features/20030815/kennerly_01.shtml

  • Kludas, J.: Information fusion for multimedia: exploiting feature interactions for semantic feature selection and construction. PhD thesis, University of Geneva, Geneva (2011)

  • Kludas J., Bruno E., Marchand-Maillet S.: Can feature information interaction help for information fusion in multimedia problems?. Multimedia Tools Appl. 42(1), 57–71 (2009)

    Article  Google Scholar 

  • Levillain, F., Orero, J.O., Rifqi, M., Bouchon-Meunier, B.: Characterizing player’s experience from physiological signals using fuzzy decision trees. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG), pp. 75–82. IT University of Copenhagen, Denmark (2010)

  • Ludford, P.J., Terveen, L.G.: Does an individual’s Myers-Briggs type indicator preference influence task-oriented technology use? In: Human-Computer Interaction INTERACT ’03: IFIP, pp. 623–630. IOS Press, Zurich(2003)

  • Malone, T.W.: What makes things fun to learn? Heuristics for designing instructional computer games. In: Proceedings of the 3rd ACM SIGSMALL Symposium and the First SIGPC Symposium on Small Systems, Palo Alto, CA, pp. 162–169 (1980)

  • McCrae R.R., Costa P.T. Jr.: Reinterpreting the Myers-Briggs Type indicator from the perspective of the five-factor model of personality. J. Pers. 57(1), 17–40 (1989)

    Article  Google Scholar 

  • McGinnis, T., Bustard, D. W., Black, M., Charles, D.: Enhancing e-learning engagement using design patterns from computer games. In: Proceedings of the First International Conference on Advances in Computer–Human Interaction, pp. 124–130. ACM, New York (2008)

  • Mitchell, T.M.: Machine Learning: McGraw-Hill Higher Education, New York (1997)

  • Mountain, G.: Psychology Profiling in SILENT HILL: SHATTERED MEMORIES. Video Presented at from the Paris Game/AI Conference, 2010. http://gameaiconf.com/?p=141

  • Mugambi E.M., Hunter A., Oatley G., Kennedy L.: Polynomial-fuzzy decision tree structures for classifying medical data. Knowledge-Based Syst. 17(2-4), 81–87 (2004)

    Article  Google Scholar 

  • Pittenger D.J.: The Utility of the Myers-Briggs type indicator. Rev. Educ. Res. 63(4), 467–488 (1993)

    Article  Google Scholar 

  • Quinlan J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)

    Google Scholar 

  • Ravaja N., Saari T., Turpeinen M., Laarni J., Salminen M., Kivikangas M.: Spatial presence and emotions during video game-playing: does it matter with whom you play?. Presence Teleoper. Virtual Environ. 15(4), 381–392 (2006)

    Article  Google Scholar 

  • Salen K., Zimmerman E.: Rules of Play: Game Design Fundamentals, vol. 1. MIT, London (2004)

    Google Scholar 

  • Sklansky D., Malmuth M.: Hold’em Poker for Advanced Players. Two Plus Two Publishing, Las Vegas (1999)

    Google Scholar 

  • Thawonmas R., Ho J.-Y.: Classification of online game-players using action transition probability and Kullback Leibler entropy. JACIII 11(3), 319–326 (2007)

    Google Scholar 

  • Thurau, C., Bauckhage, C.: Smarter team mates: applying hidden Markov models in sports games. In: Proceedings of the SAB Workshop on Adaptive Approaches to Optimizing Player Satisfaction, Roma, Italy, pp. 11–20 (2006)

  • Tipping, M., Hatton, M.: Drivatar Theory. Online article (2006). http://research.microsoft.com/en-us/projects/drivatar/theory.aspx. Retrieved 7 Sep 2011

  • Togelius, J., De Nardi, R., Lucas, S.M.: Making racing fun through player modeling and track evolution. In: Proceedings of the SAB Workshop on Adaptive Approaches to Optimizing Player Satisfaction, Roma, Italy, pp. 61–70 (2006)

  • Tuffery S.: Data Mining and Statistics for Decision Making. Wiley, Chichester (2011)

    Book  MATH  Google Scholar 

  • Tveit, A., Tveit, G.B.: Game usage mining: information gathering for knowledge discovery in massively multiplayer games. In: Proceedings of the International Conference On Internet Computing (IC’2002), Session On Web Mining, vol. III, pp. 636–642 (2002)

  • Wong C.-o., Kim J., Jung K., Han E.: Modeling for style-based adaptive games. J. Zhejiang Univ. Sci. A 10(4), 530–534 (2009)

    Article  Google Scholar 

  • Zhou, X., Conati, C.: Inferring user goals from personality and behavior in a causal model of user affect. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 211–218 (2003)

Download references

Author information

Authors and Affiliations

  1. Cognitive Science Unit, Department of Behavioural Science, University of Helsinki, PL 9, 00014, Helsinki, Finland

    Ben Cowley

  2. School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland

    Darryl Charles, Michaela Black & Ray Hickey

  3. Center for Knowledge and Innovation Research, Aalto Business School, Aalto University, Helsinki, Finland

    Ben Cowley

Authors
  1. Ben Cowley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Darryl Charles
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michaela Black
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ray Hickey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ben Cowley.

Electronic Supplementary Material

The Below is the Electronic Supplementary Material.

ESM 1 (DOC 43 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cowley, B., Charles, D., Black, M. et al. Real-time rule-based classification of player types in computer games. User Model User-Adap Inter 23, 489–526 (2013). https://doi.org/10.1007/s11257-012-9126-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-012-9126-z

Keywords

Search

Navigation