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Reinforcement Learning and the Creative, Automated Music Improviser

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

Automated creativity, giving a machine the ability to originate meaningful new concepts and ideas, is a significant challenge. Machine learning models make advances in this direction but are typically limited to reproducing already known material. Self-motivated reinforcement learning models present new possibilities in computational creativity, conceptually mimicking human learning to enable automated discovery of interesting or surprising patterns. This work describes a musical intrinsically motivated reinforcement learning model, built on adaptive resonance theory algorithms, towards the goal of producing humanly valuable creative music. The capabilities of the prototype system are examined through a series of short, promising compositions, revealing an extreme sensitivity to feature selection and parameter settings, and the need for further development of hierarchical models.

Keywords

  • Computational creativity
  • machine learning
  • music
  • composition
  • reinforcement learning
  • adaptive resonance theory

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© 2012 Springer-Verlag Berlin Heidelberg

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Smith, B.D., Garnett, G.E. (2012). Reinforcement Learning and the Creative, Automated Music Improviser. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_20

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  • DOI: https://doi.org/10.1007/978-3-642-29142-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

  • eBook Packages: Computer ScienceComputer Science (R0)