Autopoiesis in creativity and art

The term autopoiesis, (meaning ‘self’) and ‘poiesis’ (mean- ing ‘creation, production’) defines a system capable of repro- ducing and maintaining itself. The term was introduced by the theoretical biologists, Humberto Maturana and Francisco Varela, in 1972 to define the self-maintaining chemistry of living cells. The term has subsequently also been applied to the fields of systems theory and sociology. In this paper we apply this model to characterise creativity in art practise

Swarmic paintings and colour attention

Swarm-based multi-agent systems have been deployed in non-photorealistic rendering for many years. This paper introduces a novel approach in adapting a swarm intelligence algorithm – Stochastic Diffusion Search – for producing non-photorealistic images. The swarm-based system is presented with a digital image and the agents move throughout the digital canvas in an attempt to satisfy the dynamic roles – attention to different colours - associated to them via their fitness function. Having associated the rendering process with the concepts of ‘attention’ in general and colour attention in particular, this papers briefly discusses the ‘computational creativity’ of the work through two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence’s two infamous phases of exploration and exploitation

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

This paper proposes an umbrella deployment of swarm intelligence algorithm such as Stochastic Diffusion Search for medical imaging applications. After summarising the results of some previous work which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this paper is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. Additionally, a hybrid swarm intelligence-Learning Vector Quantisation (LVQ) approach is proposed in the context of Magnetic Resonance (MR) brain image segmentation. The Particle Swarm Optimisation (PSO) is used to train the LVQ which eliminates the iteration- dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images

Swarmic sketches and attention mechanism

This paper introduces a novel approach deploying the mechanism of ‘attention’ by adapting a swarm intelligence algorithm – Stochastic Diffusion Search – to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of another swarm intelligence algorithm – Particle Swarm Intelligence – is used to produce a ‘swarmic sketch’ of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles – attention to areas with more details – associated to them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the ‘artist’ swarms embark on interpreting the input line drawings. The detailed investigation of the ‘creativity’ of such systems have been explored in our previous work; nonetheless, this papers provides a brief account of the ‘computational creativity’ of the work through two prerequisites of creativity within the swarm intelligence’s two infamous phases of exploration and exploitation; these phases are described herein through the attention and tracing mechanisms respectively