Abstract
Interactive training is well suited to computer games as it allows game designers to interact with otherwise autonomous learning algorithms. This paper investigates the outcome of a group of five commercial first person shooter game designers using a custom built interactive training tool to train first person shooter bots. The designers are asked to train a bot using the tool, and then comment on their experiences. The five trained bots are then pitted against each other in a deathmatch scenario. The results show that the training tool has potential to be used in a commercial environment.
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References
Sanchez-Crespo Dalmau, D.: Core Techniques and Algorithms in Game Programming. New Riders, Indianapolis (2003)
Isla, D.: Handling Complexity in the Halo 2 AI. In: Proceedings of the Games Developers Conference. International Game Developers Association, San Francisco (2005)
Jones, J.: Benefits of Genetic Algorithms in Simulations for Game Designers. School of Informatics. University of Buffalo, Buffalo (2003)
Overholtzer, C.A., Levy, S.D.: Adding Smart Opponents to a First-Person Shooter Video Game through Evolutionary Design. In: Artificial Intelligence and Interactive Digital Entertainment. AAAI Press, USA (2005)
Petrakis, S., Tefas, A.: Neural Networks Training for Weapon Selection in First-Person Shooter Games. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 417–422. Springer, Heidelberg (2010)
van Hoorn, N., Togelius, J., Schmidhuber, J.: Hierarchical Controller Learning in a First-Person Shooter. In: Computational Intelligence and Games, pp. 294–301. IEEE Press, Milano (2009)
Spronck, P.: Adaptive Game AI. Dutch Research School of Information and Knowledge Systems. University of Maastricht, Maastricht (2005)
McPartland, M., Gallagher, M.: Reinforcement Learning in First Person Shooter Games. In: Computational Intelligence and AI in Games, pp. 43–56. IEEE Press, Perth (2011)
McPartland, M., Gallagher, M.: Interactive Training For First Person Shooter Bots. In: Computational Intelligence in Games. IEEE Press, Granada (2012)
First-Person Shooter Games Prove to be Most Popular at MTV Game Awards. Entertainment Close - Up (2011)
Geisler, B.: An Empirical Study of Machine Learning Algorithms Applied to Modelling Player Behavior in a First Person Shooter Video Game. University of Wisconsin, Madison (2002)
Mora, A.M., Montoya, R., Merelo, J.J., Sánchez, P.G., Castillo, P.Á., Laredo, J.L.J., Martínez, A.I., Espacia, A.: Evolving Bot AI in UnrealTM . In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 171–180. Springer, Heidelberg (2010)
Cole, N., Louis, S.J., Miles, C.: Using a Genetic Algorithm to Tune First-Person Shooter Bots. In: Congress on Evolutionary Computation, pp. 139–145. IEEE Press, Portland (2004)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Busoniu, L., Babuska, R., De Schutter, B.: A Comprehensive Survey of Multiagent Reinforcement Learning. Systems, Man and Cybernetics Part C: Applications and Reviews, 156–172 (2008)
Suh, I.H., Lee, S., Young Kwon, W., Cho, Y.-J.: Learning of Action Patterns and Reactive Behaviour Plans via a Novel Two-Layered Ethology-Based Action Selection Mechanism. In: International Conference on Intelligent Robot and Systems, pp. 1799–1805. IEEE Press, Edmonton (2005)
Bradley, J., Hayes, G.: Group Utility Functions: Learning Equilibria Between Groups of Agents in Computer Games By Modifying the Reinforcement Signal. In: Congress on Evolutionary Computation, pp. 1914–1921. IEEE Press, Edinburgh (2005)
Nason, S., Laird, J.E.: Soar-RL: Integrating Reinforcement Learning with Soar. Cognitive Systems Research 6(1), 51–59 (2005)
Patel, P.G., Carver, N., Rahimi, S.: Tuning Computer Gaming Agents using Q-Learning. In: Computer Science and Information Systems, pp. 581–588. IEEE Press, Szczecin (2011)
Blumberg, B., Downie, M., Ivanov, Y.A., Berlin, M., Johnson, M.P., Tomlinson, B.: Integrated Learning for Interactive Synthetic Characters. ACM Transactions on Graphics 21(3), 417–426 (2002)
Thomaz, A.L., Breazeal, C.: Reinforcement Learning with Human Teachers: Evidence of Feedback and Guidance with Implications for Learning Performance. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1000–1005. AAAI, USA (2006)
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McPartland, M., Gallagher, M. (2012). Game Designers Training First Person Shooter Bots. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_34
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DOI: https://doi.org/10.1007/978-3-642-35101-3_34
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