publication . Conference object . Preprint . 2017

General Video Game AI: Learning from screen capture

Kamolwan Kunanusont; Simon M. Lucas; Diego Perez-Liebana;
Open Access
  • Published: 23 Apr 2017
  • Publisher: IEEE
Comment: Proceedings of the IEEE Conference on Evolutionary Computation 2017
Persistent Identifiers
ACM Computing Classification System: ComputingMilieux_PERSONALCOMPUTING
free text keywords: Computer Science - Artificial Intelligence, Artificial intelligence, business.industry, business, Learning agent, Convolution, General game playing, computer.software_genre, computer, Video game, Artificial general intelligence, Screen capture, Feature extraction, Visualization, Computer science
26 references, page 1 of 2

[1] S. M. Lucas, “Ms Pac-man Competition,” ACM SIGEVOlution, vol. 2, no. 4, pp. 37-38, 2007.

[2] J. Togelius, S. Karakovskiy, and R. Baumgarten, “The 2009 Mario AI Competition,” in IEEE Congress on Evolutionary Computation, pp. 1-8, IEEE, 2010.

[3] B. Goertzel and C. Pennachin, Artificial General Intelligence, vol. 2. Springer, 2007.

[4] D. Perez, S. Samothrakis, J. Togelius, T. Schaul, S. Lucas, A. Coue¨toux, J. Lee, C.-U. Lim, and T. Thompson, “The 2014 General Video Game Playing Competition,” 2015.

[5] J. Levine, C. B. Congdon, M. Ebner, G. Kendall, S. M. Lucas, R. Miikkulainen, T. Schaul, and T. Thompson, “General Video Game Playing,” Dagstuhl Follow-Ups, vol. 6, 2013.

[6] T. Schaul, “A Video Game Description Language for Model-based or Interactive Learning,” in Computational Intelligence in Games (CIG), 2013 IEEE Conference on, pp. 1-8, IEEE, 2013. [OpenAIRE]

[7] S. M. Lucas, “Ms. Pac-man Competition: Screen Capture Version.”, 2007. Accessed: 2016-12-31.

[8] M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jas´kowski, “ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning,” arXiv preprint arXiv:1605.02097, 2016.

[9] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., “Human-level Control through Deep Reinforcement Learning,” Nature, vol. 518, no. 7540, pp. 529-533, 2015.

[10] M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling, “The Arcade Learning Environment: An Evaluation Platform for General Agents,” Journal of Artificial Intelligence Research, 2012.

[11] M. Genesereth and M. Thielscher, “General Game Playing,” Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 8, no. 2, pp. 1-229, 2014.

[12] C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A Survey of Monte Carlo Tree Search Methods,” IEEE Trans. on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1-43, 2012. [OpenAIRE]

[13] D. Perez, S. Samothrakis, and S. Lucas, “Knowledge-based Fast Evolutionary MCTS for General Video Game Playing,” in 2014 IEEE Conference on Computational Intelligence and Games, pp. 1-8, 2014.

[14] D. Perez Liebana, J. Dieskau, M. Hunermund, S. Mostaghim, and S. Lucas, “Open Loop Search for General Video Game Playing,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 337-344, ACM, 2015.

[15] D. Perez-Liebana, S. Samothrakis, J. Togelius, S. M. Lucas, and T. Schaul, “General Video Game AI: Competition, Challenges and Opportunities,” in 30 AAAI Conference on Artificial Intelligence, 2016.

26 references, page 1 of 2
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