Abstract
Most of game development along the years has been focused on the technical part (graphics and sound), leaving the artificial intelligence aside. However computational intelligence is becoming more significant, leading to much research on how to provide non-playing characters with adapted and unpredictable behaviour so as to afford users a better gaming experience. This work applies strategies based on Genetic Algorithms mixed with behavioural models, to obtain an agent (or bot) capable of completing autonomously different scenarios on a simulator of Super Mario Bros. game. Specifically, the agent follows the rules of the Gameplay track of Mario AI Championship. Different approaches have been analysed, combining Genetic Algorithms with Finite State Machines, yielding agents which can complete levels of different difficulties playing much better than an expert human player.
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Notes
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Designer and producer of Nintendo Ltd., and winner of the 2012 Príncipe de Asturias Prize in Humanities and Communication
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Acknowledgements
This work has been supported in part by the P08-TIC-03903 and P10-TIC-6083 projects awarded by the Andalusian Regional Government, the FPU Grant 2009-2942 and the TIN2011-28627-C04-01 and TIN2011-28627-C04-02 projects, awarded by the Spanish Ministry of Science and Innovation.
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Hidalgo-Bermúdez, R.M., Rodríguez-Domingo, M.S., Mora, A.M., García-Sánchez, P., Merelo, J.J., Fernández-Leiva, A.J. (2013). Evolutionary FSM-Based Agents for Playing Super Mario Game. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_39
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DOI: https://doi.org/10.1007/978-3-642-44973-4_39
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