Self-refining games using player analytics

Abstract

Data-driven simulation demands good training data drawn from a vast space of possible simulations. While fully sampling these large spaces is infeasible, we observe that in practical applications, such as gameplay, users explore only a vanishingly small subset of the dynamical state space. In this paper we present a sampling approach that takes advantage of this observation by concentrating precomputation around the states that users are most likely to encounter. We demonstrate our technique in a prototype self-refining game whose dynamics improve with play, ultimately providing realistically rendered, rich fluid dynamics in real time on a mobile device. Our results show that our analytics-driven training approach yields lower model error and fewer visual artifacts than a heuristic training strategy.

References

1. Barbič, J., and James, D. 2005. Real-time subspace integration for St. Venant-Kirchhoff deformable models. ACM Trans. Graph. 24, 3 (July), 982--990.
2. Barbič, J., and Popović, J. 2008. Real-time control of physically based simulations using gentle forces. ACM Trans. Graph. 27, 5 (Dec.), 163:1--163:10.
3. Chentanez, N., and Müller, M. 2010. Real-time simulation of large bodies of water with small scale details. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '10, 197--206.
4. Chentanez, N., and Müller, M. 2011. Real-time Eulerian water simulation using a restricted tall cell grid. ACM Trans. Graph. 30, 4 (July), 82:1--82:10.
5. Cinematronics, 1983. Dragon's Lair. {Arcade}.
6. Claypool, M., and Claypool, K. 2010. Latency can kill: Precision and deadline in online games. In Proceedings of the First ACM Multimedia Systems Conference.
7. Cooper, S., Hertzmann, A., and Popović, Z. 2007. Active learning for real-time motion controllers. ACM Trans. Graph. 26, 3 (July).
8. Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., and Popović, Z. 2010. Predicting protein structures with a multiplayer online game. Nature 466 (August).
9. Crane, K., Llamas, I., and Tariq, S. 2007. Real Time Simulation and Rendering of 3D Fluids. Addison-Wesley, ch. 30.
10. Eitz, M., Hays, J., and Alexa, M. 2012. How do humans sketch objects? ACM Trans. Graph. 31, 4 (July), 44:1--44:10.
11. El-Nasr, M. S. 2007. Interaction, narrative, and drama: Creating an adaptive interactive narrative using performance arts theories. Interaction Studies 8, 2 (June), 209--240.
12. Feller, W. 1968. An Introduction to Probability Theory and Its Applications. Wiley.
13. Guan, P., Reiss, L. and Hirshberg, D., Weiss, A., and Black, M. J. 2012. DRAPE: DRessing Any PErson. ACM Trans. Graph. 31, 4 (July), 35:1--35:10.
14. Gupta, M., and Narasimhan, S. G. 2007. Legendre fluids: A unified framework for analytic reduced space modeling and rendering of participating media. In Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '07, 17--25.
15. Houlette, R. 2003. Player modeling for adaptive games. In AI Game Programming Wisdom 2, S. Rabin, Ed. Charles River Media.
16. Jakob, W., 2010. Mitsuba renderer. http://www.mitsuba-renderer.org.
17. James, D. L., and Fatahalian, K. 2003. Precomputing interactive dynamic deformable scenes. Tech. Rep. CMU-RI-TR-03-33, Carnegie Mellon University Robotics Institute.
18. Kavan, L., Gerszewski, D., Bargteil, A. W., and Sloan, P.-P. 2011. Physics-inspired upsampling for cloth simulation in games. ACM Trans. Graph. 30, 4 (July), 93:1--93:10.
19. Kim, T., and Delaney, J. 2013. Subspace fluid re-simulation. ACM Trans. Graph. 32, 4 (July), 62:1--62:9.
20. Kim, T., and James, D. L. 2009. Skipping steps in deformable simulation with online model reduction. ACM Trans. Graph. 28, 5 (Dec.), 123:1--123:9.
21. Kim, D., Koh, W., Narain, R., Fatahalian, K., Treuille, A., and O'Brien, J. F. 2013. Near-exhaustive precomputation of secondary cloth effects. ACM Trans. Graph. 32, 4 (July), 87:1--7.
22. Kittur, A., Chi, E. H., and Suh, B. 2008. Crowdsourcing user studies with Mechanical Turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08, 453--456.
23. Lee, J., Kladwang, W., Lee, M., Cantu, D., Azizyan, M., Kim, H., Limpaecher, A., Yoon, S., Treuille, A., Das, R., and EteRNA Participants. 2014. RNA design rules from a massive open laboratory. Proceedings of the National Academy of Sciences. 2014. (Preprint).
24. Limpaecher, A., Feltman, N., Treuille, A., and Cohen, M. 2013. Real-time drawing assistance through crowdsourcing. ACM Trans. Graph. 32, 4 (July), 54:1--54:8.
25. Macklin, M., and Müller, M. 2013. Position based fluids. ACM Trans. Graph. 32, 4 (July), 104:1--104:12.
26. McCann, J., and Pollard, N. 2007. Responsive characters from motion fragments. ACM Trans. Graph. 26, 3 (July).
27. Microsoft, 2013. Drivatar website. http://research.microsoft.com/en-us/projects/drivatar.
28. Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The PageRank citation ranking: bringing order to the Web. Technical Report 1999-66, Stanford InfoLab, November.
29. Schödl, A., Szeliski, R., Salesin, D. H., and Essa, I. 2000. Video textures. In Proceedings of SIGGRAPH 2000, Computer Graphics Proceedings, Annual Conference Series, 489--498.
30. Smith, A. M., Lewis, C., Hullett, K., Smith, G., and Sullivan, A. 2011. An inclusive taxonomy of player modeling. Tech. Rep. UCSC-SOE-11-13, University of California, Santa Cruz.
31. Solenthaler, B., and Pajarola, R. 2009. Predictive-corrective incompressible SPH. ACM Trans. Graph. 28, 3 (July), 40:1--40:6.
32. Stanton, M., Sheng, Y., Wicke, M., Perazzi, F., Yuen, A., Narasimhan, S., and Treuille, A. 2013. Non-polynomial Galerkin projection on deforming meshes. ACM Trans. Graph. 32, 4 (July), 86:1--86:14.
33. Št'ava, O., Beneš, B., Brisbin, M., and Křivánek, J. 2008. Interactive terrain modeling using hydraulic erosion. In Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '08, 201--210.
34. Thue, D., Bulitko, V., Spetch, M., and Wasylishen, E. 2007. Interactive storytelling: A player modelling approach. In The Third Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE '07.
35. Thurey, N., Müller-Fischer, M., Schirm, S., and Gross, M. 2007. Real-time breaking waves for shallow water simulations. In Proceedings of the 15th Pacific Conference on Computer Graphics and Applications, PG '07, 39--46.
36. Treuille, A., Lewis, A., and Popović, Z. 2006. Model reduction for real-time fluids. ACM Trans. Graph. 25, 3 (July), 826--834.
37. von Ahn, L., and Dabbish, L. 2004. Labeling images with a computer game. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '04, 319--326.
38. von Ahn, L., Liu, R., and Blum, M. 2006. Peekaboom: a game for locating objects in images. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '06, 55--64.
39. von Ahn, L., Maurer, B., Mcmillen, C., Abraham, D., and Blum, M. 2008. reCAPTCHA: Human-based character recognition via web security measures. Science 321, 5895 (August), 1465--1468.
40. Wicke, M., Stanton, M., and Treuille, A. 2009. Modular bases for fluid dynamics. ACM Trans. Graph. 28, 3 (July), 39:1--39:8.
41. Zhu, Y., and Bridson, R. 2005. Animating sand as a fluid. ACM Trans. Graph. 24, 3 (July), 965--972.
42. Zook, A., Lee-Urban, S., Drinkwater, M. R., and Riedl, M. O. 2012. Skill-based mission generation: A data-driven temporal player modeling approach. In Proceedings of the 7th International Conference on the Foundations of Digital Games, FDG '12.
43. Zook, A., Fruchter, E., and Riedl, M. O. 2014. Automatic playtesting for game parameter tuning via active learning. In Proceedings of the 9th International Conference on the Foundations of Digital Games, FDG '14.

Index Terms

• Computing methodologies

  • Computer graphics

    • Animation

  • Machine learning

    • Machine learning approaches

      • Stochastic games

• Theory of computation

  • Theory and algorithms for application domains

    • Algorithmic game theory and mechanism design