Skip to main content

Aesthetics, Artificial Intelligence, and Search-based Art

Johnson, Colin G. (2019) Aesthetics, Artificial Intelligence, and Search-based Art. In: Romero, Juan and Machado, Penousal and Greenfield, Gary, eds. The Handbook of Artificial Intelligence and the Arts. Springer, Heidelberg. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only
Contact us about this Publication Download (367kB)
[img]

Abstract

Why do people exhibit particular behaviour towards a class of objects called artworks? That is the topic of study in aesthetics. This paper explores how various theories of aesthetics can be interpreted in the context of artworks generated by artificial intelligence systems, in particular those that are grounded in the idea of search as a means of implementing intelligence computationally. A number of aesthetic theories are explored, including ideas of imitation, skill, form, expression, imagination, and focus. The paper concludes by highlighting a number of areas that, in light of these considerations, have been neglected by the makers of computer art systems and which provide future opportunities in this area.

Item Type: Book section
Uncontrolled keywords: Artificial intelligence, computational creativity, computational arts, visual arts
Subjects: N Visual Arts > N Visual arts (General). For photography, see TR
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 20 Sep 2019 16:13 UTC
Last Modified: 23 Sep 2019 09:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/76668 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
  • Depositors only (login required):

Downloads

Downloads per month over past year