CREATIVITY AND COGNITION A study of creativity within the framework of cognitive science , artificial intelligence and the dynamical system theory (original) (raw)

How to Study Artificial Creativity

In this paper, we describe a novel approach to developing computational models of creativity that supports the multiple approaches to the study of artificial creative systems. The artificial creativity approach to the development of computational models of creative systems is described with reference to Csikszentmihalyi’s systems view of creativity. Some interesting results from studies using an early implementation of an artificially creative system, The Digital Clockwork Muse, are presented. The different studies show how the artificial creativity approach supports the study of creativity from a variety of standpoints that mirror the disciplines that study human creativity. The use of artificial creativity allows these different studies to be conveniently conducted on the same computational model and integrated in to a more complete picture of the creative process.

Artificial Creative Systems: Completing the Creative Cycle

Computational Creativity: An …

Human creativity is personally, socially and culturally situated: creative individuals work within environments rich in personal experiences, social relationships and cultural knowledge. Computational models of creative processes typically neglect some or all of these aspects of human creativity. How can we hope to capture this richness in computational models of creativity? This paper introduces recent work at the Design Lab where we are attempting to develop a model of artificial creative systems that can combine important aspects at personal, social and cultural levels.

Artificial Creativity: A Synthetic Approach to the Study of Creative Behaviour

Computational and Cognitive Models of Creative …, 2001

We present a novel approach to the computational study of creativity, called Artificial Creativity. Artificial Creativity promotes the study of the creative behaviour of individuals and societies in artificial societies of agents. It is similar to the approach to that taken by Artificial Life researchers involved in developing computational models. We present a framework for developing Artificial Creativity systems as an adaptation of Liu’s dual generate-and-test model of creativity. An example implementation of an Artificial Creativity system is presented to illustrate the potential benefits of our new approach as a way of investigating the emergent nature of creativity in societies of communicating agents. Finally, we discuss some future research directions that are possible by extending the abilities of individuals and studying the emergent behaviour of societies.

Artificial intelligence and the arts: toward computational creativity

2016

(EurAI). He serves on a variety of panels and advisory committees for public and private institutions based in the USA and Europe. MORE ABOUT THE AUTHOR + Opening image: Martial Raysse America, America (1964) Neon, painted metal 2.4 × 1.65 × 0.45 m Centre Pompidou-Musée national d'art moderne-Centre de création industrielle, Paris, France. Ramón López de Mántaras Arti cial Intelligence and the Arts: Toward Computational Creativity With this understanding in mind, an operational, and widely accepted, definition of creativity is: "A creative idea is a novel and valuable combination of known ideas." In other words, physical laws, theorems, musical pieces can be generated from a finite set of existing elements and, therefore, creativity is an advanced form of problem solving that involves memory, analogy, learning, and reasoning under constraints, among others, and is therefore possible to replicate by means of computers. This article addresses the question of the possibility of achieving computational creativity through some examples of computer programs capable of replicating some aspects of creative behavior. Due to space limitations we could not include other interesting areas of application such as: storytelling (Gervás, 2009), poetry (Montfort et al., 2014), science (Langley et al., 1987), or even humor (Ritchie, 2009). Therefore, the paper addresses, with different levels of detail, representative results of some achievements in the fields of music and visual arts. The reason for focusing on these artistic fields is that they are by far the ones in which there is more activity and where the results obtained are most impressive. The paper ends with some reflections on the recent trend of democratization of creativity by means of assisting and augmenting human creativity. For further reading regarding computational creativity in general, I recommend the AI Magazine special issue on Computational Creativity (Colton et al., 2009), as well as the books

The creative being and being creative: human and machine neural networks

The SAGE Handbook of Gifted and Talented Education, 2018

While high level human creativity is increasingly understood as an atypical deviant functioning of intelligence, machine creativity is conversely understood to be an artificial intelligence (AI) functioning deviance. This issue remains unresolved. After reviewing the research on creativity, we conclude that artificial intelligence is superior to the human brain in almost all levels and types of creativity, except the "emergent" creativity level, where the human brain usually reaches the threshold of madness. Human creativity is more a matter of deviance, while machine creativity is more a matter of intelligence. The challenge is the cooperation of the human and machine neural networks on the highest levels of creativity. Cooperation could be achieved by the acceptance of diversity in the different ways people think and learn; it will be possible to act jointly with the machine's neural networks and to be able to become the creative being.

Synergetics of human creativity. In: Dynamics, Synergetics, Autonomous Agents. Nonlinear Systems Approaches to Cognitive Psychology and Cognitive Science / Ed. by W.Tschacher, J.-P.Dauwalder. Singapore: World Scientific, 1999. P.64-79.

Dynamics, synergetics, autonomous …, 1999

The heuristic value of the synergetic models of evolution and self-organization of complex systems in the field of epistemology is shown in this contribution. Two main synergetic notions are under consideration: the notion of the discreteness of evolutionary paths of complex systems and the notion of very fast evolutionary processes, so called blow-up regimes. Creative thinking and the functioning of creative intuition and productive imagination are viewed in the light of synergetics as a process of self-organization and of self-completion of images and thoughts, filling up gaps in the net of knowledge. Insight, fast and sudden solutions to scientific problems, instabilities when 'an idea is in the air', a boom of research in a certain scientific area are all interpreted as blow-up regimes in a cognitive field.

Towards Autonomous Creative Systems: A Computational Approach

Cognitive Computation, 2012

This paper reviews the long-standing debate surrounding the nature of machine intelligence, autonomy and creativity and argues for an approach to developing autonomous computational creativity that models personal motivations, social interactions and the evolution of domains. The implications of this argument on the types of cognitive processes that are required for the development of autonomous computational creativity are explored and a possible approach to achieving the goal is described. In particular, this paper describes the development of artificial creative systems composed of intrinsically motivated agents engaging in language games to interact with a shared social and cultural environment. The paper discusses the implications that this type of approach may have for the development of autonomous creative systems. Keywords autonomy • computational creativity • artificial creative systems • systems theories of creativity • autopoiesis • intrinsic motivation • language games • evolution of language This research has been partly supported by the Australian Research Council, Discover Grant DP0666584.