Modelling the underlying principles of human aesthetic preference in evolutionary art

Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to study human visual preference through observation of nearly 500 user sessions with a simple evolutionary art system. The progress of a set of aesthetic measures throughout each interactive user session is monitored and subsequently mimicked by automatic evolution in an attempt to produce an image to the liking of the human user

Visual Hallucination For Computational Creation

Abstract Research on computational painters usually focuses on simulating rational parts of the generative process. From an art-historic perspective it is plausible to assume that also an arational process, namely visual hallucination, played an important role in modern fine art movements like Surrealism. The present work investigates this connection between creativity and hallucination. Using psychological findings, a three-step process of perception-based creativity is derived to connect the two phenomena. Insights on the neurological correlates of hallucination are used to define properties necessary for modelling them. Based on these properties a recent technique for feature visualisation in Convolutional Neural Networks is identified as a computational model of hallucination. Contrasting the thus enabled perception-based approach with the Painting Fool allows to introduce a distinction between two distinct creative acts, sketch composition and rendering. The contribution of this work is threefold: First, a computational model of hallucination is presented and discussed in the context of a computational painter. Second, a theoretic distinction is introduced that aligns research on different strands of computational creativity and captures the differences to current computational painters. Third, the case is made that computational methods can be used to simulate abnormal mental patterns, thus investigating the role that "madness" might play in creativity -instead of simply renouncing the myth of the mad artist

The Design and Evaluation of an Ambient Biofeedback Display

People use non-verbal cues, such as facial expressions, body language and tonal variations in speech, to help communicate emotion; however, these cues are not always available in interactive computer environments. For example, in computer-mediated communication, these cues don’t exist, and in interactive art, it is difficult to convey and represent emotion. Without being able to effectively communicate emotion, we can have difficulty relating to other people, and can lack self-regulation of our own emotional states. In this thesis, we propose to use abstract visual representations of emotion when regular emotion cues either don’t exist or are not appropriate to the medium. Through pilot testing and two user studies, we create abstract visual representations of emotional state and show that the visualizations are naturally interpretable and suitable for at-a-glance understanding. Finally, to demonstrate their utility, we incorporate the visual representations of emotion into a biofeedback task using ambient displays. We show that participants are able to use the visualizations to self-regulate their physiological arousal

Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing

Timing is everything: A spatio-temporal approach to the analysis of facial actions

This thesis presents a fully automatic facial expression analysis system based on the Facial Action Coding System (FACS). FACS is the best known and the most commonly used system to describe facial activity in terms of facial muscle actions (i.e., action units, AUs). We will present our research on the analysis of the morphological, spatio-temporal and behavioural aspects of facial expressions. In contrast with most other researchers in the field who use appearance based techniques, we use a geometric feature based approach. We will argue that that approach is more suitable for analysing facial expression temporal dynamics. Our system is capable of explicitly exploring the temporal aspects of facial expressions from an input colour video in terms of their onset (start), apex (peak) and offset (end). The fully automatic system presented here detects 20 facial points in the first frame and tracks them throughout the video. From the tracked points we compute geometry-based features which serve as the input to the remainder of our systems. The AU activation detection system uses GentleBoost feature selection and a Support Vector Machine (SVM) classifier to find which AUs were present in an expression. Temporal dynamics of active AUs are recognised by a hybrid GentleBoost-SVM-Hidden Markov model classifier. The system is capable of analysing 23 out of 27 existing AUs with high accuracy. The main contributions of the work presented in this thesis are the following: we have created a method for fully automatic AU analysis with state-of-the-art recognition results. We have proposed for the first time a method for recognition of the four temporal phases of an AU. We have build the largest comprehensive database of facial expressions to date. We also present for the first time in the literature two studies for automatic distinction between posed and spontaneous expressions

Art-ificial: The Philosophy of AI Art

This thesis aims to contribute to a novel area of philosophical work: the philosophy of AI art. AI art is proliferating online and increasingly in the world of art. The growing presence of works made by (or with) artificial intelligence has led to a clamour of questions such as 'Is AI art really art?' and 'can AI be truly creative?'. As yet, these questions have barely been tackled in the philosophical literature, especially in aesthetics. This thesis aims to address this gap. This thesis starts by establishing what we mean by 'AI art' by examining examples of AI works and the technological underpinnings of these systems. Existing work on the topic of AI art is explicated. In particular, Mark Coeckelbergh's three questions on AI art scaffold the first three chapters of the thesis: 'can machines create art?', 'can machines create art?' and 'can machines create art?' Chapter 1 aims to answer the question of whether AI can make art through the evaluation of AI works against three definitions of art: the institutional account, the historical account, and the cluster account. Chapter 2 focusses on the question of whether AI can be creative. Three accounts of creativity are utilised: a Darwinian theory of creativity, Margaret Boden's account of creativity, and Berys Gaut's agential account of creativity. It is argued that some AI systems can meet the requirements of each of these, aside from Gaut's necessary criterion of agency. After chapters 1 and 2, questions about the limitations of AI systems in meeting the requirements of different accounts of art and creativity remain; chapter 3 aims to address some of these. The possibility of AI (extended) mind is investigated, followed by AI embodiment. Finally, an argument for the possibility of AI agency is put forward. This minimal account of agency allows for the possibility of AI creativity under the agential account. The latter part of this thesis begins with chapter 4, which examines the possibility that AI systems will not share aesthetic or artistic values with humans, and whether this is cause for concern. Finally, Chapter 5 examines two qualities of AI images: weirdness and convincingness, showing that AI art can offer interesting aesthetic qualities worthy of investigation. Through this thesis, I put forward a first step in developing a philosophy of AI art