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Forecasting Player Behavioral Data and Simulating In-Game Events

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Advances in Information and Communication Networks (FICC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 886))

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Abstract

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that even though the performance of traditional approaches, such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors.

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References

  1. El-Nasr, M.S., Drachen, A., Canossa, A.: Game Analytics. Sprint, New York (2013)

    Book  Google Scholar 

  2. Yannakakis, G.N., Togelius, J.: Artificial Intelligence and Games. Springer (2017). http://gameaibook.org

  3. De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)

    Article  Google Scholar 

  4. Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2016)

    Book  Google Scholar 

  5. Adhikari, R., Agrawal, R.: An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013)

  6. Asmussen, S., Glynn, P.W.: Stochastic Simulation: Algorithms and Analysis, vol. 57. Springer Science and Business Media, Heidelberg (2007)

    MATH  Google Scholar 

  7. Carson, Y., Maria, A.: Simulation optimization: methods and applications. In: Proceedings of the 29th Conference on Winter Simulation, pp. 118–126. IEEE Computer Society (1997)

    Google Scholar 

  8. Box, G.E., Jenkins, G.M.: Time series analysis: forecasting and control, revised ed. Holden-Day (1976)

    Google Scholar 

  9. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  10. Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models, vol. 43. CRC Press, Boca Raton (1990)

    MATH  Google Scholar 

  11. Busseti, E., Osband, I., Wong, S.: Deep learning for time series modeling. Technical report, Stanford University (2012)

    Google Scholar 

  12. Bauckhage, C., Kersting, K., Sifa, R., Thurau, C., Drachen, A., Canossa, A.: How players lose interest in playing a game: an empirical study based on distributions of total playing times. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 139–146. IEEE (2012)

    Google Scholar 

  13. Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K., Bauckhage, C.: Predicting player churn in the wild. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)

    Google Scholar 

  14. Periáñez, Á., Saas, A., Guitart, A., Magne, C.: Churn prediction in mobile social games: towards a complete assessment using survival ensembles. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 564–573. IEEE (2016)

    Google Scholar 

  15. Bertens, P., Guitart, A., Periáñez, Á.: Games and big data: a scalable multi-dimensional churn prediction model. In: Accepted in IEEE CIG (2017)

    Google Scholar 

  16. Bauckhage, C., Drachen, A., Sifa, R.: Clustering game behavior data. IEEE Trans. Comput. Intell. AI Games 7(3), 266–278 (2015)

    Article  Google Scholar 

  17. Drachen, A., Sifa, R., Bauckhage, C., Thurau, C.: Guns, swords and data: clustering of player behavior in computer games in the wild. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 163–170. IEEE (2012)

    Google Scholar 

  18. Drachen, A., Thurau, C., Sifa, R., Bauckhage, C.: A comparison of methods for player clustering via behavioral telemetry. arXiv preprint arXiv:1407.3950 (2014)

  19. Sifa, R., Bauckhage, C., Drachen, A.: The playtime principle: large-scale cross-games interest modeling. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)

    Google Scholar 

  20. Saas, A., Guitart, A., Periáñez, Á.: Discovering playing patterns: time series clustering of free-to-play game data. In: 2016 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2016)

    Google Scholar 

  21. Lawrence, K.D., Geurts, M.D.: Advances in Business and Management Forecasting, vol. 4. Emerald Group Publishing, Bingley (2006)

    Google Scholar 

  22. Box, G.E., Cox, D.R.: An analysis of transformations. J. Roy. Statist. Soc. Ser. B (Methodol.) 26, 211–252 (1964)

    MATH  Google Scholar 

  23. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)

    Article  MathSciNet  Google Scholar 

  24. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  MathSciNet  Google Scholar 

  25. Cragg, J.G.: Estimation and testing in time-series regression models with heteroscedastic disturbances. J. Econom. 20(1), 135–157 (1982)

    Article  MathSciNet  Google Scholar 

  26. Dietterich, T.G.: Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems, pp. 1–15. Springer (2000)

    Google Scholar 

  27. Mason, L., Baxter, J., Bartlett, P.L., Frean, M.R.: Boosting algorithms as gradient descent. In: NIPS, pp. 512–518 (1999)

    Google Scholar 

  28. Breiman, L.: “Arcing the edge,” Technical Report 486, Statistics Department. University of California at Berkeley, Technical report (1997)

    Google Scholar 

  29. Ridgeway, G.: Generalized boosted models: a guide to the gbm package. Update 1(1), 2007 (2007)

    Google Scholar 

  30. Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013)

    Article  Google Scholar 

  31. Zhang, T., Yu, B.: Boosting with early stopping: convergence and consistency. Ann. Stat. 33(4), 1538–1579 (2005)

    Article  MathSciNet  Google Scholar 

  32. Hastie, T., Tibshirani, R.: Generalized additive models: some applications. J. Am. Stat. Assoc. 82(398), 371–386 (1987)

    Article  Google Scholar 

  33. Maindonald, J.: Smoothing terms in GAM models (2010)

    Google Scholar 

  34. Larsen, K.: GAM: the predictive modeling silver bullet. Multithreaded. Stitch Fix, vol. 30 (2015)

    Google Scholar 

  35. Chen, C.: Generalized additive mixed models. In: Communications in Statistics-Theory and Methods, vol. 29, no. 5–6, pp. 1257–1271 (2000)

    Article  Google Scholar 

  36. Breslow, N.E., Clayton, D.G.: Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88(421), 9–25 (1993)

    MATH  Google Scholar 

  37. Wood, S.N.: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 73(1), 3–36 (2011)

    Article  MathSciNet  Google Scholar 

  38. Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  Google Scholar 

  39. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends® Sig. Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  40. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  41. Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)

    Google Scholar 

  42. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  43. Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cognit. Sci. 9(1), 147–169 (1985)

    Article  Google Scholar 

  44. Larochelle, H., Bengio, Y.: Classification using discriminative restricted Boltzmann machines. In: Proceedings of the 25th International Conference on Machine Learning, pp. 536–543. ACM (2008)

    Google Scholar 

  45. Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42, 11–24 (2014)

    Article  Google Scholar 

  46. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)

    Article  Google Scholar 

  47. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  48. Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 78. ACM (2004)

    Google Scholar 

  49. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2005)

    Article  Google Scholar 

  50. Fox, A.J.: Outliers in time series. J. Roy. Stat. Soc. Ser. B (Methodol.) 11, 350–363 (1972)

    MathSciNet  MATH  Google Scholar 

  51. Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)

    Google Scholar 

  52. Japan national holidays. https://www.timeanddate.com/holidays/japan/

  53. Tokyo daily temperature 2014 to 2017. https://www.wunderground.com/

  54. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)

    Google Scholar 

  55. Eilers, P.H., Marx, B.D.: Flexible smoothing with B-splines and penalties. Stat. Sci. 11, 89–102 (1996)

    Article  MathSciNet  Google Scholar 

  56. Wood, S.N.: Generalized Additive Models: An Introduction with R. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  57. Wood, S.N.: Thin plate regression splines. J. R. Stat. Soc. Ser. B Stat. Methodol. 65(1), 95–114 (2003)

    Article  MathSciNet  Google Scholar 

  58. Prechelt, L.: Early stopping-but when? In: Neural Networks: Tricks of the trade, pp. 55–69. Springer (1998)

    Google Scholar 

  59. Gilliland, M., Sglavo, U., Tashman, L.: Business Forecasting: Practical Problems and Solutions. Wiley, Hoboken (2016)

    Google Scholar 

  60. Makridakis, S., Hibon, M.: The M3-Competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)

    Article  Google Scholar 

  61. Julkunen, J.: Feature Spotlight: In-Game Events and Market Trends (2016). http://www.gamerefinery.com/in-game-events-market-trends/

  62. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  63. Dwyer, L., Gill, A., Seetaram, N.: Handbook of Research Methods in Tourism: Quantitative and Qualitative Approaches. Edward Elgar Publishing, Cheltenham (2012)

    Book  Google Scholar 

  64. Khandakar, Y., Hyndman, R.J.: Automatic time series forecasting: the forecast Package for R (2008)

    Google Scholar 

  65. Wood, S.N.: MGCV: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation (2012)

    Google Scholar 

  66. Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions, arXiv e-prints, abs/1605.02688, http://arxiv.org/abs/1605.02688, May 2016

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Acknowledgments

We thank Sovannrith Lay for helping to gather the data and Javier Grande for his careful review of the manuscript.

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Authors and Affiliations

  1. Yokozuna Data unit, Silicon Studio, 1-21-3 Ebisu, Shibuya-ku, Tokyo, Japan

    Anna Guitart, Pei Pei Chen, Paul Bertens & África Periáñez

Authors
  1. Anna Guitart
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  2. Pei Pei Chen
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  3. Paul Bertens
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  4. África Periáñez
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Corresponding author

Correspondence to Anna Guitart .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Guitart, A., Chen, P.P., Bertens, P., Periáñez, Á. (2019). Forecasting Player Behavioral Data and Simulating In-Game Events. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_19

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