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What is generative art?
Abstract
There are various forms of what's sometimes called generative art, or computer art. This paper distinguishes the major categories and asks whether the appropriate aesthetic criteria—and the locus of creativity—are the same in each case.
... To illustrate the current discourse, we will shortly review the main existing frameworks on analysing generative art, being those of Galanter (2003;2008;, Boden &Edmonds (2009), andDorin et al. (2012). Galanter (2003;2008) proposes to view generative art from a systems and complexity theory influenced paradigm. ...
... To illustrate the current discourse, we will shortly review the main existing frameworks on analysing generative art, being those of Galanter (2003;2008;, Boden &Edmonds (2009), andDorin et al. (2012). Galanter (2003;2008) proposes to view generative art from a systems and complexity theory influenced paradigm. ...
... Moreover, an explanation of how these systems are applied in the artwork and what the generative properties of these complex systems attribute to the artwork as a whole is largely left untouched in Galanter's theory, which we consider crucial for achieving a critical understanding of the generative in art. Boden & Edmonds (2009) introduced a list of eleven subcategories of computer art, ranging from "Ele-art" (involving electrical engineering and/or electronic technology), to "Evo-art" (evolved by processes of random variation and selective reproduction that affect the art-generating program itself) (Boden & Edmonds 2009, 37), and question for every category "whether the appropriate aesthetic criteria and locus of creativity are the same" (Boden & Edmonds 2009, 21). Compared to Galanter, we think that their view holds a broader notion of what constitutes the artwork, as they speak of an art system of which "the artist, the program, the technological installation (and its observable results), and the behaviour of the human audience" (Boden & Edmonds 2009, 40) are all part. ...
As result of the current generative art boom, many generative works are flooding the art and media sphere. However, meaningful analysis , comparison, and discussion of generative art have so far been complicated by two factors: 1) the commonly used definition of generative art is broadly defined, resulting in a large variety of works sharing the same heading, and 2) existing methods for classifying and comparing generative art only facilitate a descriptive analysis of the generative systems within an artwork, but neglect the role & contribution of those systems to the work as a whole. In this paper we propose an alternative framework for analysing generative art, to aid the understanding of what generative art includes, where 'the gener-ative' aspect(s) in a work take(s) place, how the generative relates to other aspects in that work, and how this differs from the generative elements and aspects in other works. Two concepts are introduced: autonomous ability (AA) and artistic significance (AS), including a larger framework to analyse artworks along these concepts. The framework asks 1) what elements (generative and non-generative) the work consists of, 2) what the role of these elements is within the artwork, 3) how autonomous these elements are, and 4) how artistically significant the contribution of the element's role is in relation to the artwork as a whole. We apply the framework to a selection of four generative artworks to test its working, present the corresponding results, and reflect upon the framework.
... Computer programs are common tools for producing dynamic visual art because they can create complex and interactive animations that might be difficult to produce with traditional methods. Generative art, which emerged in the 1960s, is a type of visual art that can be created programmatically [3]. It is defined as any art practice in which the artist uses a system, such as a set of natural language rules, a computer program, a machine, or another procedural invention, with some degree of autonomy to contribute to or produce a completed work of art [4]. ...
Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience’s aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.
... There is a neuralgic point in the theory that deals with Generative Art and refers to the digital art, more specifically the use of computers. Margaret Boden and Ernest Edmonds [2] agree that generative art need not be restricted to art made using computers, and that some rule-based art is not generative. They have gone further and proposed a technical vocabulary that includes Ele-art (electronic art), C-art (computer art), D-art (digital art), CA-art (computer-aided art), Gart (generative art), CG-art (computer-based generative art), Evo-art (evolution-based art), R-art (robotic art), I-art (interactive art), CI-art (computer-based digital art), and VR-art (virtual reality art). ...
This paper is dedicated to the analysis of two works produced by two contemporary artists, more specifically "Rachael is not real" by Matthias Winckelmann and "Machine Halluci-nation", by Refik Anadol, having as convergence point the generative method of production employed in both. The article describes both works using, as theoretical foundation, the most widely accepted definition of Generative Art and reveals nuances of both works that might not be fully comprehended by the current theory. Finally, the article looks at some of the most recent technological advances and their impact on the art world, such as Artificial Intelligence, which have been used to create art, and proposes a reflection on the importance of re-visiting theoretical definitions in order for them to be able to keep up with the technical evolution of art.
This chapter examines selected philosophical, psychological, and cognitive theories of learning pertaining to the traditional program development, examines characteristics of current learning environment, and puts forward some educational propositions that may be of service in the global K-20 schooling. The focus is put on a need for integrative learning with an iterative model of introducing concepts and information, introduction of universal languages such as Latin, Music, and Mathematics, teaching and learning visually with the use of visualization techniques, teaching coding in various computer languages, instruction in serious gaming, inclusion of virtual reality into school environment, and teaching on the go through the use of social platforms for global exchange of thought. The proposed instructional design model focuses on developing skills that correspond to the needs and expectations typical of present-day society.
Since the 1980s, deconstruction has become a popular approach for designing architecture. In music, however, the term has not been absorbed as well by the related literature, with a few exceptions. In this article, ways to find ideological groundings for deconstructivism in music are introduced through the concepts of enchaînement and reconstruction paradoxes. Similar to the Banach–Tarski paradox in mathematics, reconstruction paradoxes occur when reconstructing the parts of a whole no longer yields the same properties as the whole. In music, a reconstruction paradox occurs when a piece constructed from tonal segments no longer yields a perceived tonality. Deconstruction in architecture heavily relies on computer-aided design (CAD) to realise complex ideas. Similarly in music, computer-aided composition (CAC) techniques such as neural networks, concatenative synthesis and automated orchestration are used. In this article, we discuss such tools in the context of this advocated new aesthetics: deconstructivist music.
This collection of essays examines the philosophical and cultural aspects of technology. The issues range widely - from quantum technology to problems of technology and culture in a developing country and contributors approach the issues from a variety of perspectives. The volume includes case-studies, and also more theoretical pieces which consider the fundamental question of whether technology should be perceived as a force for liberation or enslavement. The volume aims to stimulate debate about the relation between technology and philosophy and society in general, and to open a field of enquiry that has been relatively neglected. Written in an accessible style, the contributions are intended equally for philosophers exploring the novel problems arising in the twentieth and twenty-first centuries, but also for technologists interested in the philosophical implications of their work.
Rule-Based Programming is a broad presentation of the rule-based programming method with many example programs showing the strengths of the rule-based approach. The rule-based approach has been used extensively in the development of artificial intelligence systems, such as expert systems and machine learning. This rule-based programming technique has been applied in such diverse fields as medical diagnostic systems, insurance and banking systems, as well as automated design and configuration systems.
Rule-based programming is also helpful in bridging the semantic gap between an application and a program, allowing domain specialists to understand programs and participate more closely in their development.
Over sixty programs are presented and all programs are available from an ftp site. Many of these programs are presented in several versions allowing the reader to see how realistic programs are elaborated from `back of envelope' models. Metaprogramming is also presented as a technique for bridging the `semantic gap'.
Rule-Based Programming will be of interest to programmers, systems analysts and other developers of expert systems as well as to researchers and practitioners in artificial intelligence, computer science professionals and educators.
A client visited my studio recently to see what new work I had. But instead of buying a painting, she invested about a quarter of what a painting would have cost on a new computer, a data projector and a license to run my computer program, AARON, from the output of which the paintings had been made. If you were to visit her apartment in New York now, you would find one wall of her living room serving as a screen on which you would catch AARON busily generating original images, a new one every two minutes or so.
How is it possible to think new thoughts? What is creativity and can science explain it? And just how did Coleridge dream up the creatures of The Ancient Mariner? When The Creative Mind: Myths and Mechanisms was first published, Margaret A. Boden's bold and provocative exploration of creativity broke new ground. Boden uses examples such as jazz improvisation, chess, story writing, physics, and the music of Mozart, together with computing models from the field of artificial intelligence to uncover the nature of human creativity in the arts. The second edition of The Creative Mind has been updated to include recent developments in artificial intelligence, with a new preface, introduction and conclusion by the author. It is an essential work for anyone interested in the creativity of the human mind.