Is there computational creativity?

Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) intervention from the user, and without any discussion about how the unfolding decisions are taking place. Fruitful co-creation requires a sustained ongoing interaction that can include discussions of ideas, comparisons to previous/other works, incremental improvements and revisions, etc. For these interactions, communication is an intrinsic factor. This means giving a voice to CC systems and enabling bi-directional communication channels between them and their users so that they can: explain their processes and decisions, support their ideas so that these are given serious consideration by their creative collaborators, and learn from these discussions to further improve their creative processes. For this, we propose a set of design principles for CC systems that aim at supporting greater co-creation and collaboration with their human collaborators.

This paper addresses the problem of understanding the creative process in creative computing. It describes the levels of abstraction that operate in the process of musical composition. It examines the operation of these levels in the composition of Secret Garden, an installation piece by Martin Rieser and Andrew Hugill. It presents a graph-theoretic model of the creative process of that work, building on the research of Zedan, Cau, Westendorf, Buss, Hugill and Thomas. It suggests how this might be applied in creative computing.

Creativity has become a crucial topic in several disciplines such as computational creativity. Co-creativity is a rapidly growing research field in designing creative systems and artificial intelligence (AI) agents. Co-creative systems can use neuroscience to detect temporal activations to both control aspects of the user interaction and validate creativity theories. This work briefly introduces co-creative and computational creativity, presents the recent works of co-creative agent. It also introduces my future study of investigating divergent and convergent thinking in developing co-creative systems, reports my progress in research and doctoral program

Given the relatively new pedagogic contexts of creative business education and creative industry business schools, this chapter aims to examine the complexity tied-up in the word ‘creativity’ through the cultural and historical narratives that have both influenced, and been influenced by, the modern study of creativity—and more recently computational creativity and artificial intelligence (AI). The chapter illustrates that despite creativity appearing in modern dictionaries only after Guilford’s inaugural speech as chair of the APA in 1950, its modern origins belie Western historic myths and cultural assumptions such as genius, intelligence and imagination that have endured into present day ‘confluence theories’ of creativity by social psychologists such as Amabile—models largely about creativity as intrinsic psychology and cognitive abilities. It is argued that along this historical journey towards an ontology of creativity ‘in the mind’, the creativity of the person in social systems (first outlined by Whitehead in the 1920s) has been overlooked, especially in the new field of AI. Rising to a call from Still and D’Inverno’s paper at an International Conference of Computational Creativity (2016), the chapter reviews the facets and features of what they term ‘G creative’ (perhaps God like) and ‘N creative’ (perhaps nature like)—and point to the problem of psychology’s hijacking of the subject of creativity from 1950s onwards with its adopted closed system intelligence in the development of AI programs and ‘thinking’ machines. The case is made for a place in the creative academy, therefore, to help the field of AI move beyond ‘closed system’ creativity AI by pointing to more ‘open system’ approaches, including a recent AI artwork algorithm employing Csikszentmihalyi’s sociocultural creativity model of external ‘rating’.

Misconceptions about the nature of the computing discipline(s) pose a serious problem to faculties that offer computing degrees, as students enrolling on their programmes come to realise that their expectations are not met by reality. This frequently results in the students' early disengagement from the subject of their degrees which in turn can lead to excessive 'wastage', i.e. reduced retention. In this paper we report on our academic group's attempts within creative computing degrees at a UK university to counter these problems through the introduction of a six week long project that newly enrolled students embark on at the very beginning of their studies. This group project provides a breadth-first, activity-led introduction to their chosen academic discipline, aiming to increase student engagement while providing a stimulating learning experience with the overall goal to increase retention. Having run in two iterations, we believe that this approach has been successful, with students showing increased interest in their chosen discipline and noticable improvements in retention following the first year of the students' studies.

This paper focuses on explicit attempts at developing artificial intelligence in the production of art that generate outcomes similar to, or even technically superseding, the works of human artists. We aim at revealing the underlying discourses that equate art production with transformation of information, artists with input/output systems, and artistic creativity with an unlimited and autonomous generation of art-like outcomes. As a point of departure, we begin from an exposition of Margaret Boden’s account of creativity and proceed by examining different arguments to the effect that computers can be truly creative, primarily those offered by Boden (2004, 2010). We question what the assumptions, operative in the discourse on artificial or computational creativity, entail. AI-agents can produce creative outcomes because they implement our best models of creativity. By implementing these models, however, AI-agents evidence a particular understanding of what art is and what constitutes artistic production. This understanding does not fully conform to how contemporary artistic practices are perceived and valued. As a result, we argue, better models to frame artistic AI and computational creativity are needed to fully appreciate the developments in this field and their articulation within the existing art world.

This paper describes a thesis exploring how computer programs can collaborate as equals in the artistic creative process. The proposed system, CoCo Sketch, encodes some rudimentary stylistic rules of abstract sketching and music theory to contribute supplemental lines and music while the user sketches. We describe a three-part research method that includes defining rudimentary stylistic rules for abstract line drawing, exploring the interaction design for artistic improvisation with a computer, and evaluating how CoCo Sketch affects the artistic creative process. We report on the initial results of early investigations into artistic style that describe cognitive, perceptual, and behavioral processes used in abstract artists making.

The purpose of this thesis is the modification of the direction of self-creation, and it is an attempt to reflect the realistic condition of life through a certain form, and the expression of personal life experience. However, even though the “To See is not to Believe” series pertains to that which is invisible, the artist needs to make it perceptible to the audience and that often puts the writer in a dilemma. By establishing dialectic based on the movement cues of senses and imagination, leading to the research, I earnestly hope to find a language of creation that is suitable for me in order to find new possibilities in my personal artistic form. I also intent to blend artistic creation with everyday living, and capture the unseen imagination in everyday living through the process of artistic creation, subsequently discovering the inner depth lying in the foundation of everyday living, and going further in my creative career through it. With regard to the significance of the title “To See is not to Believe – Traversing, Transposing, Transitional State”, “to see is not to believe” suggests that the truth is not just what the eyes see; beyond the surface is another truth. “Traversing” and “Transposing” are the expressed abilities of the imagination and naked eye, i.e. the truth that the senses understand is the ability of the naked eye and the truth of the mind is the ability of the imagination. On the other hand, “Transitional State” refers to an area of uncertainty between the naked eye and the imagination when transitioning to and from reality and the mind. Thus, when ocular ability is unable to achieve the state of “seeing is believing”, it is hoped that the mental ability will allow one to see beyond the visible and feel beyond the tangible, to experience everyday living and feel life, and illuminating it through a creative form. The first chapter of this discourse is “Introduction”. It covers background and upbringing, creative motive, research purpose and research method, which involves theoretical research, and reading about artists and their creative process. The second chapter “To See is not to Believe – Traversing, Transposing, Transitional State” focuses more on describing the meaning and creative content of the works. The third chapter “Analysis of Creative Form” and the fourth chapter “Analysis of Creative Works” are explanation of my works done in graduate school, including the era in which the works were created, materials used, dimensions and narration of creative thinking. Keywords: To see is not to believe, traversing, transposing, state of transition Due to the translation from Chinese version, it is difficult to match the keywords in words instead of phrases.

The use of the Artificial Intelligence (AI) in the field of contemporary visual art has had an impact of monumental proportions regarding the way in which the artistic production is conducted, enabling artists to experiment with the ways of expression, communication as well as even innovation. In this paper, the author speaks about the possible uses of AI-based technology to develop the creativity of the human mind and reinvent the artistic process, including Generative Adversarial Networks (GANs), diffusion models and neural style transfers. The paper provides a detailed theoretical framework, which is founded on computational creativity, human-AI co-creation paradigms and cognitive-aesthetic principles, and describes the ways in which intelligent systems are able to become collaborative participants and not mere tools. The paper will provide a detailed analysis of the existing AI technologies to demonstrate the fact that they may be utilized to create high-quality visual materials, enhance stylistic diversity, and shorten the design cycle. The results indicate that AI-based art systems can be used to facilitate the efficiency of the creative process, expand creativity opportunities, and allow people to create works of art. However, the questions of the computational complexity, the bias of the data and the ownership by law remain to be crucial problems. The future of research directions is also discussed in the paper with an aim of creating an ethical, scalable, and artist-oriented AI system. Overall, this article is bound to demonstrate the transformational ability of AI in the contemporary realms of visual arts and approach a moderate course that will not eradicate the human originality and artist intent.

This interdisciplinary volume introduces new theories and ideas on creativity from the perspectives of science and art. Featuring contributions from leading researchers, theorists and artists working in artificial intelligence, generative art, creative computing, music composition, and cybernetics, the book examines the relationship between computation and creativity from both analytic and practical perspectives. Each contributor describes innovative new ways creativity can be understood through, and inspired by, computers. The book tackles critical philosophical questions and discusses the major issues raised by computational creativity, including: whether a computer can exhibit creativity independently of its creator; what kinds of creativity are possible in light of our knowledge from computational simulation, artificial intelligence, evolutionary theory and information theory; and whether we can begin to automate the evaluation of aesthetics and creativity in silico. These important, often controversial questions are contextualised by current thinking in computational creative arts practice. Leading artistic practitioners discuss their approaches to working creatively with computational systems in a diverse array of media, including music, sound art, visual art, and interactivity. The volume also includes a comprehensive review of computational aesthetic evaluation and judgement research, alongside discussion and insights from pioneering artists working with computation as a creative medium over the last fifty years. A distinguishing feature of this volume is that it explains and grounds new theoretical ideas on creativity through practical applications and creative practice. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future.

The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches.

Computational creativity is emerging as a sub-field of computer science; it attempts to make machines the subjects of artistic creations. Therefore, we examine whether machines can be considered the subjects of artistic creations through experiments on audiences’ appreciation of works by artificial intelligence (AI) and humans. According to our qualitative and quantitative findings, audiences reported no significant differences in appreciating works by AI and humans. Thus, it seems inappropriate to declare that “AI cannot be the subject of an artistic creation.” Prior to asking, “Can machines create art?” it is necessary to ask “Isn’t a human a kind of machine?” It is contradictory if we deny that humans are machines, because they are also formed by evolution, a mechanical algorithm. If humans are a type of machine, and if they also make art, eventually, this will come to mean that machines create art. In conclusion, it is not natural to deny the hypothesis, “Machines can be the subjects of artistic creations.”

Given the fragility of today’s intelligent systems, we consider the necessity of creativity in systems designed for artificial general intelligence. We examine an archetypical creativity “algorithm” suggested by Czikzentmihalyi in the context of computational systems, and, in particular consider the computability of such an algorithm. We argue that it is likely not computable, in the Turing sense, but that this need not necessarily preclude the building of computationally creative systems, and, by extension, (potentially) systems with a level of artificial general intelligence.

From dancing robots to action aesthetics: Re-examining mirror system activity as a function of the observer's experience

Artificial intelligence (AI) has bloomed in many novel fields such as computational creativity. Recently, research on automatic composition using AI technology, especially evolutionary algorithms, has received considerable promising results. Traditionally, chromosomes are represented as a series of numbers to indicate the notes for evolutionary composition. This study attempts to explore the composition styles by mining music patterns of a specific composer. The patterns are used as genes for chromosome representation. Accordingly, the composition styles are considered in generating music by evolutionary algorithms. The fitness function is based on music theory to smooth the progression between phrases. Experimental results show that the patterns mined from compositions can reflect the composer's style and benefit generating satisfactory songs by evolutionary algorithms.