The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents

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Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary video files available on request. Version commensurate with published text in PLoS Comput. Bio

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals

The evolution of modular artificial neural networks.

This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed

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

The evolutionary emergence of neural organisation in computational models of primitive organisms

Over the decades, the question why did neural organisation emerge in the way that it did? has proved to be massively elusive. Whilst much of the literature paints a picture of common ancestry the idea that a species at the root of the tree of nervous system evolution spawned numerous descendants the actual evolutionary forces responsible for such changes, major transitions or otherwise, have been less clear. The view presented in this thesis is that via interactions with the environment, neural organisation has emerged in concert with the constraints enforced by body plan morphology and a need to process information eciently and robustly. Whilst these factors are two smaller parts of a much greater whole, their impact during the evolutionary process cannot be ignored, for they are fundamentally signicant. Thus computer simulations have been developed to provide insight into how neural organisation of an articial agent should emerge given the constraints of its body morphology, its symmetry, feedback from the environment, and a loss of energy. The first major finding is that much of the computational process of the nervous system can be ooaded to the body morphology, which has a commensurate bearing on neural architecture, neural dynamics and motor symmetry. The second major finding is that sensory feedback strengthens the dynamic coupling between the neural system and the body plan morphology, resulting in minimal neural circuitry yet more ecient agent behaviour. The third major finding is that under the constraint of energy loss, neural circuitry again emerges to be minimalistic. Throughout, an emphasis is placed on the coupling between the nervous system and body plan morphology which are known in the literature to be tightly integrated; accordingly, both are considered on equal footings

Reinforcement learning of visually guided spatial goal directed movement

A range of visually guided, spatial goal directed tasks are investigated, using a computational neuroethology approach. Animats are embedded within a bounded, 2-D environment, and map a 1-D visual array, through a convolution network, to a topography preserving motor array that stochastically determines the direction of movement. Temporal difference reinforcement learning modifies the convolution network in response to a reinforcement signal received only at the goal location. Three forms of visual coding are compared: multiscale coding, where the visual array is convolved by Laplacian of Gaussian filters at a range of spatial scales before convolution to determine the motor array; rectified multiscale coding, where the multiscale array is split into positive and negative components; and intensity coding, where the unfiltered visual array is convolved to determine the motor array. After learning, animats are examined in terms of performance, behaviour and internal structure. When animats learn to approach a solitary circle, of randomly varying contrast, rectified multiscale coding animats learn to outperform multiscale and intensity coding animats in both independent and coarse scale noise conditions. Analysis of the learned internal structure shows that rectified multiscale filtering facilitates learning by enabling detection of the circle at scales least affected by noise. Cartwright and Collett (1983) showed that honeybees learn the angle subtended by a featureless landmark to guide movement to a food source at a fixed distance from the landmark, and furthermore, when tested with only the edges of the landmark, still search in the same location. In a simulation of this experiment, animats are reinforced for moving to where the angle subtended by a solitary circle falls within a certain range. Rectified multiscale filtering leads to better performing animats, with fewer hidden units, in both independent and coarse scale visual noise conditions, though for different reasons in each case. Only those animats with rectified multiscale filtering, that learn in the presence of coarse scale noise, show similar generalisation to the honeybees. Collett, Cartwright and Smith (1986) trained gerbils to search at locations relative to arrangemments of landmarks and tested their search patterns in modifications of the training arrangements. These experiments are simulated with landmark distance coded as either a 1-D intensity array, or a 2-D vector array, plus a simple compass sense. Vector coding animats significantly outperform those using intensity coding and do so with fewer hidden units. Furthermore, vector coding animats show a close match to gerbil behaviour in tests with modified landmark arrangements

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Evolution of Spiking Neural Networks for Temporal Pattern Recognition and Animat Control

I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs