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Computational Creativity and Aesthetics with Algorithmic Information Theory.

Tiasa Mondol1, Daniel G Brown2

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Summary
This summary is machine-generated.

This study applies Algorithmic Information Theory to computational creativity and aesthetics. It introduces measures like randomness deficiency, logical depth, and sophistication to quantify novelty and value in creative works.

Keywords:
Kolmogorov complexityalgorithmic information theorycomputational aestheticscomputational complexitycomputational creativitytypicality novelty and value

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Area of Science:

  • Computational creativity
  • Algorithmic Information Theory
  • Computational aesthetics

Background:

  • Existing formulations of computational aesthetics lack a rigorous theoretical foundation.
  • Algorithmic Information Theory offers a powerful framework for analyzing information and complexity.

Purpose of the Study:

  • To extend Algorithmic Information Theory to computational creativity and aesthetics.
  • To introduce new formalizations for measuring novelty, typicality, value, and artistry in creative artifacts.
  • To develop an algorithmic recipe for computational creativity.

Main Methods:

  • Utilizing Kolmogorov complexity and randomness deficiency to define artifact properties.
  • Formalizing aesthetic measures such as logical depth and sophistication.
  • Analyzing related research using developed algorithmic tools.

Main Results:

  • Demonstrated that typicality and novelty naturally arise from algorithmic definitions.
  • Established logical depth and sophistication as measures for value and artistry.
  • Integrated information theory concepts into a comprehensive framework for computational creativity.

Conclusions:

  • The proposed framework provides a robust, information-theoretic approach to computational creativity and aesthetics.
  • The developed measures offer quantitative insights into the properties of creative works.
  • This work lays the groundwork for a principled algorithmic recipe for artificial creativity.