Abstract
Being able to imitate individual players in a game can benefit game development by providing a means to create a variety of autonomous agents and aid understanding of which aspects of game states influence game-play. This paper presents a clustering and locally weighted regression method for modeling and imitating individual players. The algorithm first learns a generic player cluster model that is updated online to capture an individual’s game-play tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called Space Navigator.
M.E. Miller—The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense, or the United States Government. This work was supported in part through the Air Force Office of Scientific Research, Computational Cognition & Robust Decision Making Program (FA9550), James Lawton Program Manager.
The rights of this work are transferred to the extent transferable according to title 17 § 105 U.S.C.
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References
Argall, B., Browning, B., Veloso, M.: Learning by demonstration with critique from a human teacher. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 57–64. ACM (2007)
Bindewald, J.M., Miller, M.E., Peterson, G.L.: A function-to-task process model for adaptive automation system design. Int. J. Hum. Comput. Stud. 72(12), 822–834 (2014)
Bindewald, J.M., Peterson, G.L., Miller, M.E.: Trajectory generation with player modeling. In: Barbosa, D., Milios, E. (eds.) CANADIAN AI 2015. LNCS (LNAI), vol. 9091, pp. 42–49. Springer, Cham (2015). doi:10.1007/978-3-319-18356-5_4
Firemint Party Ltd.: Flight control, December 2011. https://itunes.apple.com/us/app/flight-control/id306220440?mt=8
Floyd, M.W., Esfandiari, B., Lam, K.: A case-based reasoning approach to imitating robocup players. In: FLAIRS Conference, pp. 251–256 (2008)
Gamez, D., Fountas, Z., Fidjeland, A.K.: A neurally controlled computer game avatar with human like behavior. IEEE Trans. Comput. Intell. AI Games 5(1), 1–14 (2013)
Holmgård, C., Liapis, A., Togelius, J., Yannakakis, G.N.: Evolving personas for player decision modeling. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)
Holmgård, C., Liapis, A., Togelius, J., Yannakakis, G.N.: Generative agents for player decision modeling in games. Foundations of Digital Games (2014)
Huang, V., Huang, H., Thatipamala, S., Tomlin, C.J.: Contrails: crowd-sourced learning of human models in an aircraft landing game. In: Proceedings of the AIAA GNC Conference (2013)
Kemmerling, M., Ackermann, N., Preuss, M.: Making Diplomacy bots individual. In: Hingston, P. (ed.) Believable Bots, pp. 265–288. Springer, Heidelberg (2012)
Liapis, A., Holmgård, C., Yannakakis, G.N., Togelius, J.: Procedural personas as critics for dungeon generation. In: Mora, A.M., Squillero, G. (eds.) EvoApplications 2015. LNCS, vol. 9028, pp. 331–343. Springer, Cham (2015). doi:10.1007/978-3-319-16549-3_27
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP (1), 2 (2009)
Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: A survey of real-time strategy game AI research and competition in StarCraft. Comput. Intell. AI Games 5(4), 293–311 (2013)
Schrum, J., Karpov, I.V., Miikkulainen, R.: Human-like combat behaviour via multiobjective neuroevolution. In: Hingston, P. (ed.) Believable Bots, pp. 119–150. Springer, Heidelberg (2012)
Smith, A.M., Lewis, C., Hullet, K., Sullivan, A.: An inclusive view of player modeling. In: The 6th International Conference on Foundations of Digital Games, pp. 301–303. ACM (2011)
Togelius, J., De Nardi, R., Lucas, S.M.: Making racing fun through player modeling and track evolution. In: Proceedings Optimizing Player Satisfaction in Computer and Physical Games, p. 61 (2006)
Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Yu, H., Riedl, M.O.: Personalized interactive narratives via sequential recommendation of plot points. IEEE Trans. Comput. Intell. AI Games 6(2), 174–187 (2014)
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Bindewald, J.M., Peterson, G.L., Miller, M.E. (2017). Clustering-Based Online Player Modeling. In: Cazenave, T., Winands, M., Edelkamp, S., Schiffel, S., Thielscher, M., Togelius, J. (eds) Computer Games. CGW GIGA 2016 2016. Communications in Computer and Information Science, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-57969-6_7
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