Abstract
General video game playing is the art of designing artificial intelligence programs that are capable of playing different video games with little domain knowledge. One of the great challenges is how to capture game state features from different video games in a general way. The main contribution of this paper is to apply genetic programming to evolve game state features from raw pixels. A voting method is implemented to determine the actions of the game agent. Three different video games are used to evaluate the effectiveness of the algorithm: Missile Command, Frogger, and Space Invaders. The results show that genetic programming is able to find useful game state features for all three games.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bellemare, M., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253–279 (2012)
Perez, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, S.: GVG-AI Competition. http://www.gvgai.net/index.php
Finnsson, H., Bjornsson, Y.: Simulation-based approach to general game playing. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, pp. 259–264 (2008)
Geneserech, M., Love, N.: General game playing: overview of the AAAI competition. AI Mag. 26, 62–72 (2005)
Guo, X., Singh, S., Lee, H., Lewis, R., Wang, X.: Deep learning for real-time atari game play using offline monte-carlo tree search planning. Adv. Neural Inf. Process. Syst. 27, 3338–3346 (2014)
Hausknecht, M., Khandelwal, P., Miikkulainen, R., Stone, P.: HyperNEAT-GGP: a HyperNEAT-based atari general game player. In: Genetic and Evolutionary Computation Conference(GECCO) (2012)
Hausknecht, M., Lehman, J., Miikkulainen, R., Stone, P.: A neuroevolution approach to general atari game playing. IEEE Trans. Comput. Intell. AI Games 6, 355–366 (2013)
Jia, B., Ebner, M., Schack, C.: A GP-based video game player. In: Genetic and Evolutionary Computation Conference(GECCO) (2015)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge (1994)
Luke, S.: The ECJ Owner’s Manual, 22nd edn. (2014)
Campbell, M., Hoane, A.J., Hsu, F.H.: Deep blue. Artif. Intell. 134, 57–83 (2002)
Mehat, J., Cazenave, T.: Monte-Carlo Tree Search for General Game Playing. Technical report, LIASD, Dept. Informatique, Université Paris 8 (2008)
Mehat, J., Cazenave, T.: Combining UCT and nested monte-carlo search for single-player general game playing. IEEE Trans. Comput. Intell. AI Games 2(4), 225–228 (2010)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A., Veness, J., Bellemare, M., Graves, A., Riedmiller, M., Fidieland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Naddaf, Y.: Game-independent AI agents for playing atari 2600 console games. Master’s thesis, University of Alberta (2010)
Perez, D., Samothrakis, S., Lucas, S.: Knowledge-based fast evolutionary MCTS for general video game playing. In: Proceedings of IEEE Conference on Computational Intelligence and Games, pp. 68–75 (2014)
Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Jia, B., Ebner, M. (2017). Evolving Game State Features from Raw Pixels. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-55696-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55695-6
Online ISBN: 978-3-319-55696-3
eBook Packages: Computer ScienceComputer Science (R0)