Influential papers (survey, tutorial, research)

• General video game ai: a multi-track framework for evaluating agents, games and content generation algorithms. Perez-Liebana, D., Liu, J., Khalifa, A., Gaina, R.D., Togelius, J. and Lucas, S.M., 2018. arXiv preprint arXiv:1802.10363.
• Game AI Revisited. Yannakakis, G.N., 2012, May. In Proceedings of the 9th conference on Computing Frontiers (pp. 285-292). ACM.

• A panorama of artificial and computational intelligence in games. Yannakakis, G.N. and Togelius, J., 2014. IEEE Transactions on Computational Intelligence and AI in Games, 7(4), pp.317-335.

• A survey of monte carlo tree search methods. Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. and Colton, S., 2012. IEEE Transactions on Computational Intelligence and AI in games, 4(1), pp.1-43.
• Bandit based monte-carlo planning. Kocsis, L. and Szepesvári, C., 2006, September. In European conference on machine learning (pp. 282-293). Springer, Berlin, Heidelberg.

• Combinatorial multi-armed bandits for real-time strategy games. Ontanón, S., 2017. Journal of Artificial Intelligence Research, 58, pp.665-702.

• Playing atari with deep reinforcement learning. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D. and Riedmiller, M., 2013. arXiv preprint arXiv:1312.5602.
• Asynchronous methods for deep reinforcement learning. Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D. and Kavukcuoglu, K., 2016, June. In International conference on machine learning (pp. 1928-1937).
• Mastering the game of Go with deep neural networks and tree search. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M. and Dieleman, S., 2016. nature, 529(7587), p.484.
• Human-level control through deep reinforcement learning. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Nature, 518(7540), p.529.
• Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. Peng, X.B., Abbeel, P., Levine, S. and van de Panne, M., 2018. ACM Transactions on Graphics (TOG), 37(4), p.143.
• A unified game-theoretic approach to multiagent reinforcement learning. Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D. and Graepel, T., 2017. In Advances in Neural Information Processing Systems (pp. 4190-4203).
• Open-ended learning in symmetric zero-sum games. Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W.M., Perolat, J., Jaderberg, M. and Graepel, T., 2019. arXiv preprint arXiv:1901.08106.

• Mastering chess and shogi by self-play with a general reinforcement learning algorithm. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T. and Lillicrap, T., 2017. arXiv preprint arXiv:1712.01815.

• Curiosity-driven Exploration by Self-supervised Prediction. Pathak, D., Agrawal, P., Efros, A.A. and Darrell, T., 2017. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 16-17).

• World models. Ha, D. and Schmidhuber, J., 2018. arXiv preprint arXiv:1803.10122.

• Computational Game Creativity. Liapis, A., Yannakakis, G.N. and Togelius, J., 2014, June. In ICCC (pp. 46-53).
• Search-based procedural content generation: A taxonomy and survey. Togelius, J., Yannakakis, G.N., Stanley, K.O. and Browne, C., 2011. IEEE Transactions on Computational Intelligence and AI in Games, 3(3), pp.172-186.
• Experience-driven procedural content generation.Yannakakis, G.N. and Togelius, J., 2011. IEEE Transactions on Affective Computing, 2(3), pp.147-161.
• Casual Creators. Compton, K. and Mateas, M., 2015, June. In ICCC (pp. 228-235).
• Procedural content generation for games: A survey. Hendrikx, M., Meijer, S., Van Der Velden, J. and Iosup, A., 2013. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 9(1), p.1.
• Procedural Content Generation in Games: A Textbook and an Overview of Current Research. Togelius, J., Shaker, N. and Nelson, M.J., 2014. Berlin: Springer.
• Procedural Content Generation: An Overview. G. Smith. In Game AI Pro 2: Collected Wisdom of Game AI Professionals (CRC Press, 2015), ch. 40, pp. 501 – 518.
• Procedural content generation via machine learning (PCGML). Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A.K., Isaksen, A., Nealen, A. and Togelius, J., 2018. IEEE Transactions on Games, 10(3), pp.257-270.

• Procedural generation of dungeons. R. van der Linden, R. Lopes and R. Bidarra. IEEE Transactions on Computational Intelligence and AI in Games 6(1) (2014) 78–89.

• Human-Computer Insurrection: Notes on an Anarchist HCI. Keyes, O., Hoy, J. and Drouhard, M., 2019, April. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 339). ACM.
• Power to the people: The Role of Humans in Interactive Machine Learning. Amershi, S., Cakmak, M., Knox, W.B. and Kulesza, T., 2014. AI Magazine, 35(4), pp.105-120.
• Mixed-Initiative Creative Interfaces Deterding, S., Hook, J., Fiebrink, R., Gillies, M., Gow, J., Akten, M., Smith, G., Liapis, A. and Compton, K., 2017, May. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 628-635). ACM.