Abstract
This paper introduces an approach based on Genetic Algorithms to learn levels from the Mario AI simulator, based on the Infinite Mario Bros. game (which is, at the same time, based on the Super Mario World game from Nintendo). In this approach, an autonomous agent playing Mario is able to learn a sequence of actions in order to maximize the score, not looking at the current state of the game at each time.
Different parameters for the Genetic Algorithm are explored, and two different stages are executed: in the first, domain independent genetic operators are used; while in the second knowledge about the domain is incorporated to these operators in order to improve the results.
Results are encouraging, as Mario is able to complete very difficult levels full of enemies, resembling the behavior of an expert human player.
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Acknowledgements
This research work is co-funded by the Spanish Ministry of Industry, Tourism and Commerce under grant agreement no. TSI-090302-2011-11. Special acknowledgements are addressed at Hector Valero due to his contributions to the work.
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Baldominos, A., Saez, Y., Recio, G., Calle, J. (2015). Learning Levels of Mario AI Using Genetic Algorithms. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_24
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DOI: https://doi.org/10.1007/978-3-319-24598-0_24
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