Abstract

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.

Highlights

  • The principal idea in Algorithmic Information theory (AIT) is that we can quantify an absolute information content in an object with an algorithm or a program that gives rise to an object on some fixed machine

  • The argument that the meaningful information content in “War and Peace” can be measured by including it in a set of possible novels with a probability distribution defined over the members, is a weak one [4]. This problem is less severe and more natural to analyze in an algorithmic setting, where a concise description of “War and Peace” would mean the description or algorithm “knows” about its global theme and at times Tolstoy’s intent. Such computationfocused method opens up ways of examining the object that goes beyond just measuring its information content: the length of the computation may point toward how information becomes buried under redundancy

  • At the end of the paper, we summarize the ideas presented, with a complete recipe based on algorithmic information theory for the fundamental concepts in creativity and aesthetics

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Summary

Introduction

The principal idea in Algorithmic Information theory (AIT) is that we can quantify an absolute information content in an object with an algorithm or a program (a set of instructions) that gives rise to an object on some fixed machine. The argument that the meaningful information content in “War and Peace” can be measured by including it in a set of possible novels with a probability distribution defined over the members, is a weak one [4] This problem is less severe and more natural to analyze in an algorithmic setting, where a concise description of “War and Peace” would mean the description or algorithm “knows” about its global theme and at times Tolstoy’s intent. The 4‘P’ perspective of creativity [6] identifies the producer, process, press (context) along with the product as important dimensions of creativity and describes how each of these components is needed for the proper assessment of a creative artifact Formalizing these entities independently of each other is challenging; with the tools from AIT, we can investigate how these creative components may be identified when our reference point is the creative object itself. Measures like this provide a firm computational underpinning of some fundamental yardsticks of creativity, and the focus of this paper is to look at a creative artifact through an algorithmic lens—taking each of the algorithm’s length, runtime, plausibility and meaningfulness into account

Motivation
Summary of Results
Kolmogorov Complexity
Algorithmic Probability
The Order and Complexity of an Artifact
Two-Part Code and Models
Finite Set Model
Total Recursive Function Model
Randomness Deficiency and Typicality
Novelty
Value as Computational Effort
Logical Depth and Its Relation to Value
The Logical Steps of a Creative Process
Sophistication and the Creator
Generative Attributes of a Creator
Non-Stochastic Objects or Masterpieces
Related Works and Discussion
A Formal Framework
Findings
Conclusions
Full Text
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