Abstract
This paper investigates the applicability of Genetic Programming type systems to dynamic game environments. Grammatical Evolution was used to evolved Behaviour Trees, in order to create controllers for the Mario AI Benchmark. The results obtained reinforce the applicability of evolutionary programming systems to the development of artificial intelligence in games, and in dynamic systems in general, illustrating their viability as an alternative to more standard AI techniques.
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References
Angeline, P.: Subtree Crossover: Building Block Engine or Macromutation? In: Proceedings of Genetic Programming 1997, pp. 9–17. Morgan Kaufmann, San Francisco (1997)
Champandard, A., Dawe, M., Cerpa, D.H.: Behavior Trees: Three Ways of Cultivating Strong AI. In: Game Developers Conference, Audio Lecture (2010)
Champandard, A.: Behavior Trees for Next-Gen Game AI. In: Game Developers Conference, Audio Lecture (2007)
Colvin, R., Hayes, I.J.: A Semantics for Behavior Trees. ARC Centre for Complex Systems. Tech. report ACCS-TR-07-01 (2007)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Isla, D.: Managing Complexity in the Halo 2 AI System. In: Proceedings of Game Developers Conference (2005)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lim, C., Baumgarten, R., Colton, S.: Evolving Behaviour Trees for the Commercial Game DEFCON. In: Proceedings of Applications of Evolutionary Computation, EvoStar 2010 (2010)
McKay, R.I., Nguyen, X.H., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-Based Genetic Programming - A Survey. Genetic Programming and Evolvable Machines 11(3-4), 365–396 (2010)
McHugh, L.: Three Approaches to Behavior Tree AI. In: Proceedings of Game Developers Conference (2007)
Mora, A.M., Montoya, R., Merelo, J.J., Sánchez, P.G., Castillo, P.A., Laredo, J.L.J., Martínez, A.I., Espacia, A.: Evolving Bot AI in Unreal. In: Proceedings of Applications of Evolutionary Computation, EvoStar 2010 (2010)
Mateas, M., Stern, A.: Managing Intermixing Behavior Hierarchies. In: Proceedings of Game Developers Conference (2004)
Nicolau, M., Dempsey, I.: Introducing Grammar Based Extensions for Grammatical Evolution. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 2663–2670. IEEE Press, Los Alamitos (2006)
Nilsson, N.J.: Artificial Intelligence, A New Synthesis. Morgan Kaufmann Publishers, San Francisco (1998)
Nason, S., Laird, J.: Soar-RL: Integrating Reinforcement Learning with Soar. In: Proceedings of International Conference on Cognitive Modelling (2004)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)
Priesterjahn, S.: Imitation-Based Evolution of Artificial Game Players. ACM Sigevolution 2(4), 2–13 (2009)
Ryan, C., Azad, R.M.A.: Sensible initialisation in grammatical evolution. In: Barry, A.M. (ed.) GECCO 2003: Proceedings of the Bird of a Feather Workshops, pp. 142–145. AAAI, Menlo Park (July 2003)
Sastry, K., O’Reilly, U., Goldberg, D.E., Hill, D.: Building Block Supply in Genetic Programming. In: Genetic Programming Theory and Practice, ch. 4, pp. 137–154. Kluwer Publishers, Dordrecht (2003)
Thurau, C., Bauckhauge, C., Sagerer, G.: Combining Self Organizing Maps and Multiplayer Perceptrons to Learn Bot-Behavior for a Comercial Game. In: Proceedings of GAME-ON 2003 Conference (2003)
Togelius, J., Karakovskiy, S., Baumgarten, R.: The 2009 Mario AI Competition. In: Proceedings of IEEE Congress on Evolutionary Computation. IEEE Press, Los Alamitos (2010)
Togelius, J., Karakovskiy, S., Koutnik, J., Schmidhuber, J.: Super Mario Evolution. In: Proceedings of IEEE Symposium on Computational Intelligence and Games. IEEE Press, Los Alamitos (2009)
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Perez, D., Nicolau, M., O’Neill, M., Brabazon, A. (2011). Evolving Behaviour Trees for the Mario AI Competition Using Grammatical Evolution. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_13
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DOI: https://doi.org/10.1007/978-3-642-20525-5_13
Publisher Name: Springer, Berlin, Heidelberg
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