The Genealogy of Biomimetics: Half a Century’s Quest for Dynamic IT

Abstract. Biologically inspired approaches to the design of IT are presently flourishing. Investigating the scientific and historical roots of the tendency will serve to prepare properly for future biomimetic work. This paper explores the genealogy of the contemporary biological influence on science, design and culture in general to determine the merits of the tendency and lessons to learn. It is argued that biomimetics rests on bona fide scientific and technical reasons for the pursuit of dynamic IT, but also on other more external factors, and that biomimetics should differentiate the relevant from the superficial. Furthermore the search for dynamic capacities of IT mimicking adaptive processes can bring is put forward as both the history and raison d’être of biomimetics. 1. Lifelike – á la mode Biology is enjoying enormous attention from different scientific fields as well as culture in general these days. Examples are legion: The victorious naturalization project in philosophy and psychology spearheaded by cognitive science in the second half of the 20th century; the exploration of biological structures in the engineering of materials or architectures [1]; a dominant trend of organismoid designs with ‘grown’ curves replacing straight lines to convey a slickness and efficiency not previously associated with life; 1 World Expo 2005 being promoted under the slogans “Nature’s Wisdom ” and “Art of Life”; 2 and biology’s new status as the successor of physics as the celebrity science which gets major funding and most headlines. These examples are neither historically unique nor culturally revolutionary. Life and nature have been fetishized before. Yet the fascination with the living has never previously dominated with such universality and impetus, as we presently experience. So we might ask: What is the reason for this ubiquitous interest in life and is it a result of cultural and scientific progress or merely an arbitrary fluctuation soon to be forgotten again? 1 Think of cars, sports apparel, furniture, mobile phones, watches, sunglasses etc

Artificial Societies of Intelligent Agents

In this thesis we present our work, where we developed artificial societies of intelligent agents, in order to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in animats. In our behaviours virtual laboratory, the user can perform a wide variety of experiments, allowing him or her to test the properties of our behaviours production system and our social action model, and also to understand adaptive and social behaviour. It can be accessed and downloaded through the Internet. Before presenting our proposals, we make an introduction to artificial intelligence and behaviour-based systems, and also we give notions of complex systems and artificial societies. In the last chapter of the thesis, we present experiments carried out in our behaviours virtual laboratory showing the main properties of our behaviours production system, of our social action model, and of our behaviours virtual laboratory itself. Finally, we discuss about the understanding of adaptive behaviour as a path for understanding cognition and its evolution

Machines That Learn: Aesthetics of Adaptive Behaviors in Agent-based Art

Since the post-war era, artists have been exploring the use of embodied, artificial agents. This artistic activity runs parallel to research in computer science, in domains such as Cybernetics, Artificial Intelligence and Artificial Life. This thesis offers an account of a particular facet of this broader work — namely, a study of the artistic practice of agent-based, adaptive computational artistic installations that make use of Machine Learning methods. Machine Learning is a sub-field of the computer science area of Artificial Intelligence that employs mathematical models to classify and make predictions based on data or experience rather than on logical rules. These artworks that integrate Machine Learning into their structures raise a number of important questions: (1) What new forms of aesthetic experience do Machine Learning methods enable or make possible when utilized outside of their intended context, and are instead carried over into artistic works? (2) What characterizes the practice of using adaptive computational methods in agent-based artworks? And finally, (3) what kind of worldview are these works fostering? To address these questions, I examine the history of Machine Learning in both art and science, illustrating how artists and engineers alike have made use of these methods historically. I also analyze the defining scientific characteristics of Machine Learning through a practitioner’s lens, concretely articulating how properties of Machine Learning interplay in media artworks that behave and evolve in real time. I later develop a framework for understanding machine behaviors based on the morphological aspects of the temporal unfolding of agent behaviors as a tool for comprehending both adaptive and non-adaptive behaviors in works of art. Finally, I expose how adaptive technologies suggest a new worldview for art that accounts for the performative engagement of agents adapting to one another, which implies a certain way of losing control in the face of the indeterminacy and the unintelligibility of alien agencies and their behaviors

OBSERVING ARTIFICIAL LIFE EVOLUTION: A FORMAL ALGEBRAIC FRAMEWORK

Master'sMASTER OF SCIENC

The Irresistible Animacy of Lively Artefacts

This thesis explores the perception of ‘liveliness’, or ‘animacy’, in robotically driven artefacts. This perception is irresistible, pervasive, aesthetically potent and poorly understood. I argue that the Cartesian rationalist tendencies of robotic and artificial intelligence research cultures, and associated cognitivist theories of mind, fail to acknowledge the perceptual and instinctual emotional affects that lively artefacts elicit. The thesis examines how we see artefacts with particular qualities of motion to be alive, and asks what notions of cognition can explain these perceptions. ‘Irresistible Animacy’ is our human tendency to be drawn to the primitive and strangely thrilling nature of experiencing lively artefacts. I have two research methodologies; one is interdisciplinary scholarship and the other is my artistic practice of building lively artefacts. I have developed an approach that draws on first-order cybernetics’ central animating principle of feedback-control, and second-order cybernetics’ concerns with cognition. The foundations of this approach are based upon practices of machine making to embody and perform animate behaviour, both as scientific and artistic pursuits. These have inspired embodied, embedded, enactive, and extended notions of cognition. I have developed an understanding using a theoretical framework, drawing upon literature on visual perception, behavioural and social psychology, puppetry, animation, cybernetics, robotics, interaction and aesthetics. I take as a starting point, the understanding that the visual cortex of the vertebrate eye includes active feature-detection for animate agents in our environment, and actively constructs the causal and social structure of this environment. I suggest perceptual ambiguity is at the centre of all animated art forms. Ambiguity encourages natural curiosity and interactive participation. It also elicits complex visceral qualities of presence and the uncanny. In the making of my own Lively Artefacts, I demonstrate a series of different approaches including the use of abstraction, artificial life algorithms, and reactive techniques

Simulations and Modelling for Biological Invasions

Biological invasions are characterized by the movement of organisms from their native geographic region to new, distinct regions in which they may have significant impacts. Biological invasions pose one of the most serious threats to global biodiversity, and hence significant resources are invested in predicting, preventing, and managing them. Biological systems and processes are typically large, complex, and inherently difficult to study naturally because of their immense scale and complexity. Hence, computational modelling and simulation approaches can be taken to study them. In this dissertation, I applied computer simulations to address two important problems in invasion biology. First, in invasion biology, the impact of genetic diversity of introduced populations on their establishment success is unknown. We took an individual-based modelling approach to explore this, leveraging an ecosystem simulation called EcoSim to simulate biological invasions. We conducted reciprocal transplants of prey individuals across two simulated environments, over a gradient of genetic diversity. Our simulation results demonstrated that a harsh environment with low and spatially-varying resource abundance mediated a relationship between genetic diversity and short-term establishment success of introduced populations rather than the degree of difference between native and introduced ranges. We also found that reducing Allee effects by maintaining compactness, a measure of spatial density, was key to the establishment success of prey individuals in EcoSim, which were sexually reproducing. Further, we found evidence of a more complex relationship between genetic diversity and long-term establishment success, assuming multiple introductions were occurring. Low-diversity populations seemed to benefit more strongly from multiple introductions than high-diversity populations. Our results also corroborated the evolutionary imbalance hypothesis: the environment that yielded greater diversity produced better invaders and itself was less invasible. Finally, our study corroborated a mechanical explanation for the evolutionary imbalance hypothesis – the populations evolved in a more intense competitive environment produced better invaders. Secondly, an important advancement in invasion biology is the use of genetic barcoding or metabarcoding, in conjunction with next-generation sequencing, as a potential means of early detection of aquatic introduced species. Barcoding and metabarcoding invariably requires some amount of computational DNA sequence processing. Unfortunately, optimal processing parameters are not known in advance and the consequences of suboptimal parameter selection are poorly understood. We aimed to determine the optimal parameterization of a common sequence processing pipeline for both early detection of aquatic nonindigenous species and conducting species richness assessments. We then aimed to determine the performance of optimized pipelines in a simulated inoculation of sequences into community samples. We found that early detection requires relatively lenient processing parameters. Further, optimality depended on the research goal – what was optimal for early detection was suboptimal for estimating species richness and vice-versa. Finally, with optimal parameter selection, fewer than 11 target sequences were required in order to detect 90% of nonindigenous species