Abstract
In digital games, the visual representation of game assets such as avatars or game levels can hint at their purpose, in-game use and strengths. In the Pokémon games, this is particularly prevalent with the namesake creatures’ type and the colors in their sprites. To win these games, players choose Pokémon of the right type to counter their opponents’ strengths; this makes the visual identification of type important. In this paper, computational intelligence methods are used to learn a mapping between a Pokémon’s type and its in-game sprite, colors and shape. This mapping can be useful for a designer attempting to create new Pokémon of certain types. In this paper, instead, evolutionary algorithms are used to create new Pokémon sprites by using existing color information but recombining it into a new palette. Results show that evolution can be applied to Pokémon sprites on a local or global scale, to exert different degrees of designer control and to achieve different goals.
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References
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, New Jersey (1944)
Perez-Liebana, D., Samothrakis, S., Togelius, J., Lucas, S.M., Schaul, T.: General video game AI: competition, challenges and opportunities. In: Proceedings of the AAAI Conference on Artificial Intelligence (2016)
Browne, C., Maire, F.: Evolutionary game design. IEEE Trans. Comput. Intell. AI Games 2(1), 1–16 (2010)
Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 99, 147–161 (2011)
Hunicke, R., Leblanc, M., Zubek, R.: MDA: a formal approach to game design and game research. In: Proceedings of the Challenges in Game AI Workshop, Nineteenth National Conference on Artificial Intelligence (2004)
Winters, G.J., Zhu, J.: Guiding players through structural composition patterns in 3D adventure games. In: Proceedings of the Foundations of Digital Games Conference (2014)
Yannakakis, G.N., Spronck, P., Loiacono, D., Andre, E.: Player modeling. In: Dagstuhl Seminar on Artificial and Computational Intelligence in Games (2013)
Lim, C.U., Liapis, A., Harrell, D.F.: Discovering social and aesthetic categories of avatars: a bottom-up artificial intelligence approach using image clustering. In: Proceedings of the International Joint Conference of DiGRA and FDG (2016)
Basri, R., Costa, L., Geiger, D., Jacobs, D.: Determining the similarity of deformable shapes. Vis. Res. 38, 15–16 (1998)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Martins, T., Correia, J., Costa, E., Machado, P.: Evotype: evolutionary type design. In: Johnson, C., Carballal, A., Correia, J. (eds.) EvoMUSART 2015. LNCS, vol. 9027, pp. 136–147. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16498-4_13
Lehman, J., Risi, S., Clune, J.: Creative generation of 3D objects with deep learning and innovation engines. In: Proceedings of the International Conference on Computational Creativity (2016)
Kao, D., Harrell, D.F.: Exigent: an automatic avatar generation system. In: Proceedings of the Foundations of Digital Games Conference (2015)
Liapis, A., Yannakakis, G.N., Togelius, J.: Computational game creativity. In: Proceedings of the Fifth International Conference on Computational Creativity (2014)
Risi, S., Lehman, J., D’Ambrosio, D.B., Hall, R., Stanley, K.O.: Combining search-based procedural content generation and social gaming in the petalz video game. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (2012)
Hastings, E.J., Guha, R.K., Stanley, K.O.: Evolving content in the galactic arms race video game. In: Proceedings of the IEEE Conference on Computational Intelligence and Games (2009)
Liapis, A., Yannakakis, G.N., Togelius, J.: Adapting models of visual aesthetics for personalized content creation. IEEE Trans. Comput. Intell. AI Games 4(3), 213–228 (2012)
Howlett, A., Colton, S., Browne, C.: Evolving pixel shaders for the prototype video game subversion. In: Proceedings of the AI and Games Symposium (AISB 2010) (2010)
Summerville, A., Mateas, M.: Sampling hyrule: multi-technique probabilistic level generation for action role playing games. In: Proceedings of the Foundations of Digital Games Conference (2015)
Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)
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Pokémon images and names are copyright of Nintendo/Game Freak; no copyright infringement is intended. No monetary profit was made from this article.
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Liapis, A. (2018). Recomposing the Pokémon Color Palette. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_22
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DOI: https://doi.org/10.1007/978-3-319-77538-8_22
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