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Generalised Player Modelling: Why Artificial Intelligence in Games Should Incorporate Meaning, with a Formalism for so Doing

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HCI in Games (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12211))

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Abstract

General game-playing artificial intelligence (AI) has recently seen important advances due to the various techniques known as ‘deep learning’. However, in terms of human-computer interaction, the advances conceal a major limitation: these algorithms do not incorporate any sense of what human players find meaningful in games.

I argue that adaptive game AI will be enhanced by a generalised player model, because games are inherently human artefacts which require some encoding of the human perspective in order to respond naturally to individual players. The player model provides constraints on the adaptive AI, which allow it to encode aspects of what human players find meaningful. I propose that a general player model requires parameters for the subjective experience of play, including: player psychology, game structure, and actions of play. I argue that such a player model would enhance efficiency of per-game solutions, and also support study of game-playing by allowing (within-player) comparison between games, or (within-game) comparison between players (human and AI).

Here we detail requirements for functional adaptive AI, arguing from first-principles drawn from games research literature, and propose a formal specification for a generalised player model based on our ‘Behavlets’ method for psychologically-derived player modelling.

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Notes

  1. 1.

    A priori knowledge includes knowledge of ‘realistic’ or ‘natural’ elements. This can help when adapting, as some changes need not be explicitly explained, such as the trivial example of player opponents that increase in toughness as they increase in size. A priori can also refer to game design patterns, existing conventions which somewhat binds developers to the forms of previous work in their chosen genre.

  2. 2.

    With a non-rational learning human player at the core of gameplay (who may display high choice variance, i.e. infer different predicates based on the same observations), game processes are usually strictly non-Markovian; however they can still be given Markovian representations as a simplifying assumption.

  3. 3.

    Although continuous systems are constrained to have finite duration of input times, they may have infinite number of inputs defined as vector field maps from an input manifold. This permits a model consistent with the player’s point of view, which is an important part of creating psychologically relevant models.

  4. 4.

    This is a rare occasion when a magic square becomes a Magic Circle (in the sense of Huizinga, not Yang Hui)!.

References

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

    Google Scholar 

  2. Bateman, C., Lowenhaupt, R., Nacke, L.: Player typology in theory and practice. In: Digital Games Research Association conference, Utrecht, Netherlands (2011)

    Google Scholar 

  3. Björk, S., Holopainen, J.: Patterns in Game Design. Charles River Media, Hingham (2005)

    Google Scholar 

  4. Breining, S., Kriegel, H.P., Schubert, M., Zufle, A.: Action sequence mining. In: Croonenborghs, T., Driessens, K., Missura, O. (eds.) Second International Workshop on Machine Learning and Data Mining in Games, at European Conference on Machine Learning, Athens, Greece (2011)

    Google Scholar 

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

    Google Scholar 

  6. Chanel, G., Rebetez, C., Bétrancourt, M., Pun, T.: Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41, 1052–1063 (2011)

    Article  Google Scholar 

  7. Cowley, B., Charles, D., Black, M., Hickey, R.: User-system-experience model for user centered design in computer games. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 419–424. Springer, Heidelberg (2006). https://doi.org/10.1007/11768012_62

    Chapter  Google Scholar 

  8. Cowley, B., Charles, D., Black, M., Hickey, R.: Toward an understanding of flow in video games. Comput. Entertain. 6(2), 1–27 (2008)

    Article  Google Scholar 

  9. Cowley, B., Charles, D., Black, M., Hickey, R.: Real-time rule-based classification of player types in computer games. User Model. User-Adapt. Interact. 23(5), 489–526 (2012). https://doi.org/10.1007/s11257-012-9126-z

    Article  Google Scholar 

  10. Cowley, B., Charles, D.: Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features. User Model. User-Adapt. Interact. 26(2), 257–306 (2016)

    Article  Google Scholar 

  11. Cowley, B.U.: How to advance general game playing artificial intelligence by player modelling. arXiv 1606.00401, June 2016

    Google Scholar 

  12. Cowley, B.U., Charles, D.: Adaptive artificial intelligence in games: issues, requirements, and a solution through Behavlets-based general player modelling. arXiv 1607.05028, July 2016

    Google Scholar 

  13. Cowley, B.U., Charles, D.: Short literature review for a general player model based on Behavlets. arXiv 1603.06996, 7 March 2016

    Google Scholar 

  14. Cowley, B.U., Charles, D.: Utility of a Behavlets approach to a decision theoretic predictive player model. arXiv 1603.08973, March 2016

    Google Scholar 

  15. Crowley, K.: Flexible strategy use in young children’s tic-tac-toe. Cogn. Sci. 17(4), 531–561 (1993)

    Article  Google Scholar 

  16. Espejo, R., Harnden, R.: The Viable System Model: Interpretations and Applications of Stafford Beer’s VSM. Wiley, New York (1989)

    Google Scholar 

  17. Gmytrasiewicz, P.J., Lisetti, C.L.: Modeling users’ emotions during interactive entertainment sessions (2000)

    Google Scholar 

  18. Grünvogel, S.: Formal models and game design. Games Stud. 5(1), 1–9 (2005)

    Google Scholar 

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

    Google Scholar 

  20. Hunicke, R., LeBlanc, M., Zubek, R.: MDA: a formal approach to game design and game research. In: Proceedings of the Challenges in Game AI Workshop. AAAI Press, Stanford University in Palo Alto, CA (2004)

    Google Scholar 

  21. Järvinen, A.: Games Without Frontiers: Methods for Game Studies and Design. VDM Verlag, Copenhagen (2009)

    Google Scholar 

  22. Koster, R.: A Theory of Fun for Game Design. Paraglyph Press, Scottsdale (2005)

    Google Scholar 

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

    Google Scholar 

  24. Malone, T.W.: What makes computer games fun? [for education]. BYTE 6(12), 258–277 (1981)

    Google Scholar 

  25. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)

    Article  Google Scholar 

  26. Rauterberg, M.: About a framework for information and information processing of learning systems. In: Falkenberg, E.D., Hesse, W., Olivé, A. (eds.) Information System Concepts. IAICT, pp. 54–69. Springer, Boston, MA (1995). https://doi.org/10.1007/978-0-387-34870-4_7

    Chapter  Google Scholar 

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

    Google Scholar 

  28. Sicart, M.: Defining game mechanics. Games Stud. 8(2) (2008)

    Google Scholar 

  29. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  30. Steinkuehler, C., Duncan, S.: Scientific habits of mind in virtual worlds. J. Sci. Educ. Technol. 17(6), 530–543 (2008)

    Article  Google Scholar 

  31. Swalwell, M., Wilson, J., Kücklich, J.: Forbidden pleasures - cheating in computer games. In: The Pleasures of Computer Gaming: Essays on Cultural History, Theory and Aesthetics. McFarland & Co., Jefferson, N.C. (2008)

    Google Scholar 

  32. Tabuada, P., Pappas, G.J., Lima, P.: Compositional abstractions of hybrid control systems. Discret. Event Dyn. Syst. 14(2), 203–238 (2004)

    Article  MathSciNet  Google Scholar 

  33. Von Neuman, J., Morgenstern, O.: Theory of Games and Economic Behavior. Wiley, New York (1944)

    Google Scholar 

  34. Walters, R.: Categories and Computer Science. Cambridge University Press, Cambridge (1991)

    MATH  Google Scholar 

  35. Yannakakis, G.N.: Game AI revisited. In: Proceedings of the 9th Conference on Computing Frontiers, CF 2012, pp. 285–292. ACM, New York (2012)

    Google Scholar 

  36. Yannakakis, G.N., Togelius, J.: A panorama of artificial and computational intelligence in games. IEEE Trans. Comput. Intell. AI Games 7(4), 317–335 (2015). https://doi.org/10.1109/TCIAIG.2014.2339221

    Article  Google Scholar 

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Authors and Affiliations

  1. Data Science, Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland

    Benjamin Ultan Cowley

  2. Cognitive Science, Faculty of Arts, University of Helsinki, Helsinki, Finland

    Benjamin Ultan Cowley

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  1. Benjamin Ultan Cowley
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Correspondence to Benjamin Ultan Cowley .

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  1. DePaul University, Chicago, IL, USA

    Xiaowen Fang

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Cowley, B.U. (2020). Generalised Player Modelling: Why Artificial Intelligence in Games Should Incorporate Meaning, with a Formalism for so Doing. In: Fang, X. (eds) HCI in Games. HCII 2020. Lecture Notes in Computer Science(), vol 12211. Springer, Cham. https://doi.org/10.1007/978-3-030-50164-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-50164-8_1

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