Reinforcement learning and the creative, automated music improviser

Benjamin D. Smith, Guy E. Garnett

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationEvolutionary and Biologically Inspired Music, Sound, Art and Design - First International Conference, EvoMUSART 2012, Proceedings
Pages223-234
Number of pages12
DOIs
StatePublished - Apr 3 2012
Event1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012 - Malaga, Spain
Duration: Apr 11 2012Apr 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7247 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
CountrySpain
CityMalaga
Period4/11/124/13/12

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Smith, B. D., & Garnett, G. E. (2012). Reinforcement learning and the creative, automated music improviser. In Evolutionary and Biologically Inspired Music, Sound, Art and Design - First International Conference, EvoMUSART 2012, Proceedings (pp. 223-234). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7247 LNCS). https://doi.org/10.1007/978-3-642-29142-5_20