The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented.

The goal of the research activities presented in this thesis is the design of intelligent behaviours for a complex robotic system, which is composed of a swarm of autonomous units. Inspired by the organisational skills of social insects, we are particularly interested in the study of collective behaviours based on self-organisation. The problem of designing self-organising behaviours for a swarm of robots is tackled resorting to artificial evolution, which proceeds in a bottom-up direction by first defining the controllers at the individual level and then testing their effect at the collective level. In this way, it is possible to bypass the difficulties encountered in the decomposition of the global behaviour into individual ones, and the further encoding of the individual behaviours into the controllers' rules. In the experiments presented in this thesis, we show that this approach is viable, as it produces efficient individual controllers and robust self-organising behaviours. To the best of our knowledge, our experiments are the only example of evolved self-organising behaviours that are successfully tested on a physical robotic platform. Besides the engineering value, the evolution of self-organising behaviours for a swarm of robots also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.

By growing together in dense communities, microorganisms (microbes) have a huge impact on human life. Microbes also come in a wide variety of shapes, but we have yet to understand the importance of these shapes for community biology. How are multi- species communities, such as biofilms and colonies, affected by the morphologies of constituent cells? Which morphologies might these environments select for in turn? To address these questions, we use individual-based modelling to investigate the effects of cell shape on patterning and evolution within microbial communities. We develop a flexible simulation framework, coupling a continuum model of the biofilm chemical environment to a cellular-level description of biofilm growth mechanics. This modelling system allows competitions between different microbial cell shapes to be simulated and studied, in different community contexts. Our models predict that cell shape can strongly affect spatial structure and patterning within competitive communities. Rod cells perform better at colonising surfaces and the expanding edges of colonies, while round cells are better at dominating the upper surface of a community. Our predictions are supported by experiments using Escherichia coli and Pseudomonas aeruginosa bacteria, and demonstrate that particular shapes can confer a selective advantage in communities. In summary, the work presented in this thesis predicts and examines new mechanisms of self-organisation driven by cell shape, demonstrating a new significance for microbial morphology as a means for cells to succeed in a dense and competitive environment.

In this thesis I present a language for instructing a sheet of identically-programmed, flexible, autonomous agents (``cells'') to assemble themselves into a predetermined global shape, using local interactions. The global shape is described as a folding construction on a continuous sheet, using a set of axioms from paper-folding (origami). I provide a means of automatically deriving the cell program, executed by all cells, from the global shape description. With this language, a wide variety of global shapes and patterns can be synthesized, using only local interactions between identically-programmed cells. Examples include flat layered shapes, all plane Euclidean constructions, and a variety of tessellation patterns. In contrast to approaches based on cellular automata or evolution, the cell program is directly derived from the global shape description and is composed from a small number of biologically-inspired primitives: gradients, neighborhood query, polarity inversion, cell-to-cell contact and flexible folding. The cell programs are robust, without relying on regular cell placement, global coordinates, or synchronous operation and can tolerate a small amount of random cell death. I show that an average cell neighborhood of 15 is sufficient to reliably self-assemble complex shapes and geometric patterns on randomly distributed cells. The language provides many insights into the relationship between local and global descriptions of behavior, such as the advantage of constructive languages, mechanisms for achieving global robustness, and mechanisms for achieving scale-independent shapes from a single cell program. The language suggests a mechanism by which many related shapes can be created by the same cell program, in the manner of D'Arcy Thompson's famous coordinate transformations. The thesis illuminates how complex morphology and pattern can emerge from local interactions, and how one can engineer robust self-assembly.

<p>This dissertation aims to provide a critical evaluation of artificial life (A-Life) models of communication in autonomous agents. In particular the focus will be on the issue of self-organisation, which is often argued to be one of the characteristic features distinguishing A-life from other approaches. To ground the arguments, a background of the study of communication within artificial intelligence is provided. This is followed by a comprehensive review of A-Life research on communication between autonomous agents, which is evaluated by breaking down self-organisation into the following sub-questions. Is communication self-organised or hard-coded? What do signals mean to the agents, and how should an external examiner interpret them? Is there any spatial or temporal displacement, or do agents only communicate about their present situation? It is shown that there is very little self-organised communication, as yet, when examined on these grounds, and that most models only look at communication as relatively independent from other behaviours. As a conclusion, it is suggested to use integrated co-evolution of behaviours, including communication, in the spirit of the enactive cognitive science paradigm, and by using incremental evolution combined with learning.</p>

This dissertation aims to provide a critical evaluation of artificial life (A-Life) models of communication in autonomous agents. In particular the focus will be on the issue of self-organisation, which is often argued to be one of the characteristic features distinguishing A-life from other approaches. To ground the arguments, a background of the study of communication within artificial intelligence is provided. This is followed by a comprehensive review of A-Life research on communication between autonomous agents, which is evaluated by breaking down self-organisation into the following sub-questions. Is communication self-organised or hard-coded? What do signals mean to the agents, and how should an external examiner interpret them? Is there any spatial or temporal displacement, or do agents only communicate about their present situation? It is shown that there is very little self-organised communication, as yet, when examined on these grounds, and that most models only look at communication as relatively independent from other behaviours. As a conclusion, it is suggested to use integrated co-evolution of behaviours, including communication, in the spirit of the enactive cognitive science paradigm, and by using incremental evolution combined with learning.

The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that can not be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. In order to keep individual robots simple, a control algorithm has to keep expensive communication to a minimum and has to be able to act on little information to keep the amount of sensors down. The number of sensors and actuators can be reduced even more when necessary capabilities are spread out over different agents that then combine them by cooperating. Self-organised differentiation within these heterogeneous groups has to take the individual abilities of agents into account to improve group performance. In this thesis it is shown that a homogeneous group of versatile agents can not be easily replaced by a heterogeneous group, by separating the abilities of the versatile agents into several specialists. It is shown that no composition of those specialists produces the same outcome as a homogeneous group on a clustering task. In the second part of this work, an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is analysed in more detail. It does not require communication and needs only the information on individual success or failure. The algorithm leads to self-organised regulation of group activity depending on object availability in the environment by adjusting resting times in a base. A possible variation of this algorithm is introduced which replaces the probabilistic mechanism with which agents determine to leave the base. It is demonstrated that a direct calculation of the resting times does not lead to differences in terms of differentiation and speed of adaptation. After investigating effects of different parameters on the system, it is shown that there is no efficiency increase in static environments with constant object density when using a homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic environments. The algorithm was also reported to lead to higher activity of agents which have higher performance. It is shown that this leads to efficiency gains in heterogeneous groups in static and dynamic environments.

Spike-timing dependent plasticity is a learning mechanism used extensively within neural modelling. The learning rule has been shown to allow a neuron to find the onset of a spatio-temporal pattern repeated among its afferents. In this thesis, the first question addressed is ‘what does this neuron learn?’ With a spiking neuron model and linear prediction, evidence is adduced that the neuron learns two components: (1) the level of average background activity and (2) specific spike times of a pattern. Taking advantage of these findings, a network is developed that can train recognisers for longer spatio-temporal input signals using spike-timing dependent plasticity. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feedforwardly connected in such a way that both the correct stimulus and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. Following this, a novel resource-based STDP learning rule is introduced. The learning rule has several advantages over typical implementations of STDP and results in synaptic statistics which match favourably with those observed experimentally. For example, synaptic weight distributions and the presence of silent synapses match experimental data.

Les travaux présentés dans ce manuscrit, sont basés sur l'étude de l'auto-organisation de la matière à l'échelle nanométrique. A cette échelle, les énergies de surfaces jouent un rôle prépondérant dans cette organisation. Pour comprendre au mieux ses mécanismes nous avons étudié plusieurs types de structures à base de Silicium et de Germanium. Nous avons expérimentalement étudié la croissance cristalline ou amorphe sur différents types de substrats (amorphe : SiO2 et cristallins Si ou SOI). Certain de ces substrats furent nano-structurés en utilisant un faisceau d'ions focalisés de type Gallium ou Or-Silicium. De plus nous avons pu utiliser des surfaces différentes telle que le TiO2 ou le Silicium poreux, afin d'étudier l'organisation de la matière sur des pores de petites tailles (inférieurs à 50nm). / The following works are base on the study of self assembly structures at the nanometric scale. At this scale the surface energy have a major impact in this organization. For a better understanding of this mechanism we studied different Silicon-Germanium base structures. We experimentally studied the crystalline or amorphous growth on different types of substrates (amorphous: SiO2, crystalline: Si or SOI). Some of these substrates were nano-structured using a focused ion beam using gallium source or gold-silicon source. In addition, we were able to use different surfaces such as TiO2 or porous silicon to study the organization of the material small size pore(less than 50nm).

Recent advances in Micro-Electro-Mechanical Systems (MEMS) and low-power short-range radios have enabled rapid development of wireless sensor networks. Future sensor networks are anticipated to include hundreds or thousands of these devices in many applications, such as capturing multimedia content for surveillance, structural health monitoring, tracking of accidental chemical leaks, machine failures, earthquakes and intrusion detection. With the increase of sensor applications, a number of challenging problems related to the network protocol design has emerged - the most important ones relating to energy efficiency and lifetime maximisation. Techniques devised for sensor networks should deal with a large number of sensors distributed in the field. Wireless sensor nodes are deployed with limited energy reserves, so the networks should operate with minimum energy overhead. In fact, the network should take into account not only individual node's energy efficiency but also consider the global picture, because surviving nodes' energy reserves in a failed network are wasted energy. This thesis examines a node organisation technique to deal with the above challenges. The focus is on improving network lifetime via organising the nodes in a distributed and energy efficient manner. The main goal is lowering wasted energy via energy balancing and exploiting node redundancy in case of node failure. In particular, this thesis proposes Energy Balanced Clustering (EBC) method for node self-organisation where network tasks (such as data aggregation and data forwarding) are shifted to high-energy neighbours to reduce the energy consumption of low energy nodes. After showing how to extend network lifetime by energy balanced node organisation, the effect of redundant node deployments on network lifetime is addressed. Redundant nodes consume energy by performing unnecessary tasks so a method called Self-Calculated Redundancy Check (SCRC) is proposed to deactivate redundant nodes. A deactivated redundant node can be used as a replacement for a failed node. The Asynchronous Failed Sensor node Detection (AFSD) proposed in this thesis uses the data packets exchanged between neighbours to identify failed neighbours. To restore coverage for network holes caused by failed nodes, policies are given for re-activating redundant nodes. Detailed analytical analysis and simulation of the proposed methods demonstrate that by taking into account energy balancing, eliminating redundant tasks and replacing failed nodes sensor network lifetime can significantly be improved.