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Learning Levels of Mario AI Using Genetic Algorithms

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Advances in Artificial Intelligence (CAEPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9422))

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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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References

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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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Authors and Affiliations

  1. Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911, Leganes, Spain

    Alejandro Baldominos, Yago Saez, Gustavo Recio & Javier Calle

Authors
  1. Alejandro Baldominos
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  2. Yago Saez
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  3. Gustavo Recio
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  4. Javier Calle
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Corresponding author

Correspondence to Alejandro Baldominos .

Editor information

Editors and Affiliations

  1. University of Castilla-La Mancha, Albacete, Spain

    José M. Puerta

  2. University of Castilla-La Mancha, Albacete, Spain

    José A. Gámez

  3. University of Cadiz, Cadiz, Spain

    Bernabe Dorronsoro

  4. Public University of Navarre, Pamplona, Spain

    Edurne Barrenechea

  5. Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso

  6. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Bruno Baruque

  7. Public University of Navarre, Pamplona, Spain

    Mikel Galar

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24597-3

  • Online ISBN: 978-3-319-24598-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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