Computational Creativity and Aesthetics with Algorithmic Information Theory

Entropy

Daniel G. Brown¹  Tiasa Mondol² 

[1] David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;Untether AI, Toronto, ON M5V 2H2, Canada;

关键词: computational creativity;    computational aesthetics;    algorithmic information theory;    Kolmogorov complexity;    typicality novelty and value;    computational complexity;   

DOI  :  10.3390/e23121654

来源: DOAJ

【 摘 要 】

We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of how much a model falls short of capturing the regularities in an artifact) and show that the notions of typicality and novelty of a creative artifact follow naturally from such definitions. Other exciting formalizations of aesthetic measures include logical depth and sophistication with which we can define, respectively, the value and creator’s artistry present in a creative work. We then look at some related research that combines information theory and creativity and analyze them with the algorithmic tools that we develop throughout the paper. Finally, we assemble the ideas and their algorithmic counterparts to complete an algorithmic information theoretic recipe for computational creativity and aesthetics.

【 授权许可】

Unknown