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Decision-making is an important operation for any autonomous system. Robots in particular must observe their environment and compute appropriate responses. For solitary robots and centralized multiple-robot systems, decision-making is a relatively straightforward operation, since only a single agent (either the solitary robot or the single central controller) is solely responsible for the operation. The problem is much more complex in a decentralized system, to the point where optimal decision-making is intractable in the general case. Decentralized multiple-robot systems (dec-MRS) are robotic teams in which no robot is in authority over any others. The globally observed behaviour of dec-MRS emerges out of the individual robots’ local interactions with each other. This makes system-level decision-making, an operation in which an entire dec-MRS cooperatively makes a decision, a difficult problem. Social insects have long been a source of inspiration for dec-MRS research, and their example is followed in this work. Honeybees and Temnothorax ants must make group decisions in order to choose a new nest site whenever they relocate their colonies. Like the simple robots that compose typical dec-MRS, the insects utilize local, peer-to-peer behaviours to make good, cooperative decisions. This thesis examines their decision-making strategies in detail and proposes a three-phase framework for system-level decision-making by dec-MRS. Two different styles of decision are described, and experiments in both simulation and with real robots were carried out and presented here to demonstrate the framework’s decision-making ability. Using only local, anonymous communication and emergent behaviour, the proposed collective decision-making framework is able to make good decisions reliably, even in the presence of noisy individual sensing. Social cues such as consensus and quorum testing enables the robots to predicate their behaviour during the decision-making process on the global state of their system. Furthermore, because the operations carried out by the individual robots are so simple, and because their complexity to the individual robots is independent of the population size of a dec-MRS, the proposed decision-making framework will scale well to very large population sizes.

L’objectif de cette thèse est de s’inspirer de la neurobiologie pour modéliser les mécanismes émotionnels de bas niveau sur un robot évoluant en environnement réel. Ce travail présente un modèle des émotions cohérent avec les données expérimentales décrivant le fonctionnement des structures cérébrales principales impliquées dans les mécanismes émotionnels. Les émotions jouent un rôle capital aussi bien pour la régulation du comportement des êtres humains que des animaux. En accord avec la vision darwinienne, les émotions sont vues comme des mécanismes adaptatifs favorisant la survie. Cependant, leur organisation autours de signaux essentiellement positifs et négatifs leur donne un caractère dimensionnel. Notre modèle considère les émotions comme le résultat de la dynamique d’interactions entre deux systèmes permettant l’évaluation des interactions avec l’environnement physique d’une part et l’environnement social d’autre part. Cette approche bioinspirée des émotions permet de donner aux robots une mécanique de base pour construire leur autonomie comportementale et leurs capacités de communication. Dans cette thèse, nous montrons qu’elles permettent autant de s’adapter aux caractéristiques de l’environnement que de servir de support à une communication non verbale. L’approche biomimétique de notre travail se traduit en termes méthodologiques par l’utilisation de réseaux de neurones formels pour les architectures de contrôle du robot mais aussi en termes fonctionnels par l’organisation de ces réseaux comme modèles de différentes structures du cerveau et de leurs interactions (amygdale, accumbens, hippocampe et cortex préfrontal). Suivant le courant animat, le robot est vu comme un animal aux besoins vitaux satisfaits par les ressources de son environnement. Les expérimentations seront illustrées sur des comportements de navigation reposant sur les apprentissages de conditionnements visuo-moteurs (stratégie visuelle) et sur l’intégration de chemin (stratégie propioceptive). Les conditionnements associant les signaux nocicepteurs et hédoniques aux autres informations sensorielles ou aux actions du robot sont à la base des régulation sémotionnelles. Les prédictions que forme le robot lui permettent d’apprendre des comportements aversifs ou appétitifs en réponse à ses anticipations de "douleur" ou de "plaisir". Il peut aussi monitorer ses prédictions afin d’évaluer l’efficacité de ses comportements. C’est ce qui lui permet de réguler ses motivations et de sélectionner ses stratégies (navigation visuelle ou proprioceptive) et ses buts (ressources de l’environnement) de façon à satisfaire au mieux son équilibre interne en fonction de son environnement. Cette utilisation de signaux bas niveau positifs et négatifs permet de construire un modèle émotionnel minimal assurant au robot une autonomie comportementale. Dans un deuxième temps, nous utilisons l’expressivité émotionnelle comme base à une communication avec le robot. Une tête mécanique permet au robot d’exprimer ses émotions grâce à ses expressions faciales. Cette communication consiste à donner au robot des signaux de récompense et de punition. Nous avons développé un modèle permettant de construire de manière autonome ces signaux d’interaction en leur donnant leur valeur émotionnelle. Cet échange d’informations avec le robot lui permet d’apprendre à valuer son environnement ou son comportement et ainsi d’apprendre interactivement à résoudre ses problèmes de navigation. / The objective of this thesis is to draw inspiration from the neurobiology to model low level emotional mechanisms on a robot evolving in real environment. This work presents an emotional model coherent with experimental data describing the functioning of the cerebral structures involved in emotional mechanisms. Emotionsplay a central part in the regulation of behavior of humans as well as animals. In agreement with the darwinian view, emotions are seen as adaptive mechanismsenhancing survival. However, their organization around essential positive and negative signals gives them a dimensional flavor. Our model considers emotions as the result of the interaction dynamics between two systems. These systems allow the evaluation of the interactions with the physical and the social environment. This bio-inspired approach of emotions gives robots a basic framework to construct their behavioral autonomy and their communication skills. In this thesis, we show that they allow the robot to adapt itself to the characteristics of the environment as well as they underlie non verbal communication. The bio-mimetic approach of this thesis is reflected in methodological terms by the use of artificial neural networks for robot control architectures but also in functional terms by the organization of these networks as models of different brain structures and their interactions (amygdala, accumbens, periaqueductal grey, hippocampus, prefrontal cortex). Following the animat paradigm, the robot is seen as an animal which vital needs are satisfied by the resources of the environment. Experimentation are conducted on navigation behaviors relying on visuo-motor conditionings (visual strategy) and on path integration (proprioceptive strategy). Conditionings between nociceptive or hedonic signals and other sensory information or actions of the robot are at the basis of emotional regulation. The robot predictions allow it to learn aversive or appetitive behavior in response to its "pain" or "pleasure" expectations. The robot can also monitor its predictions to assess the effectiveness of its behaviors. This enables it to regulate its motivations and select its strategies (visual navigation or proprioceptive) and goals (environmental resources) in order to best meet its internal balance depending on its environment. This use of low level positive and negative signals allows to build a minimal emotional model providing autonomy to the robot behavior.In a second step, we use the emotional expressiveness as the basis for communication with the robot. A mechanical head enables it to express its emotions through its facial expressions. This communication consists in giving the robot reward and punishment signals. This exchange of information with the robot allows it to learn to valuate its environment or its behavior and thus to learn interactively to solve its navigation tasks. The model of emotional mechanisms presented in this work allows to investigate issues of autonomous robotics as well as issues of Human-Robot interactions. Moreover, this approach shows the interest of putting robotics at the heart of cognitive sciences due to the perspective given by the analysis of robot’s behaviors supported by relatively simple neuronal architectures.

This research focuses on biologically inspired audio signal processing using programmable analog circuitry. This research is inspired by the biology of the human cochlea since biology far outperforms any engineered system at converting audio signals into meaningful electrical signals. The human cochlea efficiently decomposes any sound into the respective frequency components by harnessing the resonance nature of the basilar membrane, essentially forming a bank of bandpass filters. In a similar fashion, this work revolves around developing a filter bank composed of continuous-time, low-power, analog bandpass filters that serve as the core front end to this silicon audio-processing system. Like biology, the individual bandpass filters are tuned to have narrow bandwidths, moderate amounts of resonance, and exponentially spaced center frequencies. This audio front end serves to efficiently convert incoming sounds into information useful to subsequent signal-processing elements, and it does so by performing a frequency decomposition of the waveform with extremely low-power consumption and real-time operation. To overcome mismatch and offsets inherent in CMOS processes, floating-gate transistors are used to precisely tune the time constants in the filters and to allow programmability of analog components.

Networks, such as the Internet, are comprised of dense information flows with expansive, multi-directional reach that continuously change—and this changeability is what keeps the network active, relative, and vital. I call the form of network exhibiting those dynamic features the vital network. This form of network is not simply the outcome of connectivity and communication between affiliative objects and actors such as cell phones and humans that together convey a sense or feeling of ‘aliveness,’ it is the outcome of software programming goals for communication systems inspired by nonhuman, self-organizing biological life. The biological turn in computation produces an organizing logic for the vital network that self-propagates connections and disconnections, services, collectives, and structures proximal to forms that feel vital and dynamic. The vital network can do things, it has capacities to act, and different material consequences emerge out of the organization and coordination of communication with particular implications for human privacy, autonomy, and network transparency. I examine the biological turn in computing as a feature within a development program for the design of digital network control systems that rely on self-regulation and autonomous communication processes intentionally constructed to be non-transparent. I explore nonhuman models of control as a response to this requirement considered through three objects: microbe, simulation, and control, each understood in process terms that disclose what these things do and how they act. It is appropriate to the concerns of this dissertation to think of these as object-processes occurring within three moments or transverse becomings: first, in terms of Gilles Deleuze’s notion of differentiation from the one to the many; secondly, from organism to simulation through the use of models to describe microbial processes in informatic terms; and finally, from description to control through the progression in computing from an emphasis on structure and descriptive procedures, to processes of control. Given that so much of contemporary life is structured by communication technology, my study points to the need for an ethics of control to imagine how much and how deep control should go when considering the organization appropriate to our shared, technically enabled, sphere of communication. / Thesis (Ph.D, Sociology) -- Queen's University, 2014-01-27 14:57:29.139

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