A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Robot Learning and Control Using Error-Related Cognitive Brain Signals

Durante los últimos años, el campo de los interfaces cerebro-máquina (BMIs en inglés) ha demostrado cómo humanos y animales son capaces de controlar dispositivos neuroprotésicos directamente de la modulación voluntaria de sus señales cerebrales, tanto en aproximaciones invasivas como no invasivas. Todos estos BMIs comparten un paradigma común, donde el usuario trasmite información relacionada con el control de la neuroprótesis. Esta información se recoge de la actividad cerebral del usuario, para luego ser traducida en comandos de control para el dispositivo. Cuando el dispositivo recibe y ejecuta la orden, el usuario recibe una retroalimentación del rendimiento del sistema, cerrando de esta manera el bucle entre usuario y dispositivo. La mayoría de los BMIs decodifican parámetros de control de áreas corticales para generar la secuencia de movimientos para la neuroprótesis. Esta aproximación simula al control motor típico, dado que enlaza la actividad neural con el comportamiento o la ejecución motora. La ejecución motora, sin embargo, es el resultado de la actividad combinada del córtex cerebral, áreas subcorticales y la médula espinal. De hecho, numerosos movimientos complejos, desde la manipulación a andar, se tratan principalmente al nivel de la médula espinal, mientras que las áreas corticales simplemente proveen el punto del espacio a alcanzar y el momento de inicio del movimiento. Esta tesis propone un paradigma BMI alternativo que trata de emular el rol de los niveles subcorticales durante el control motor. El paradigma se basa en señales cerebrales que transportan información cognitiva asociada con procesos de toma de decisiones en movimientos orientados a un objetivo, y cuya implementación de bajo nivel se maneja en niveles subcorticales. A lo largo de la tesis, se presenta el primer paso hacia el desarrollo de este paradigma centrándose en una señal cognitiva específica relacionada con el procesamiento de errores humano: los potenciales de error (ErrPs) medibles mediante electroencefalograma (EEG). En esta propuesta de paradigma, la neuroprótesis ejecuta activamente una tarea de alcance mientras el usuario simplemente monitoriza el rendimiento del dispositivo mediante la evaluación de la calidad de las acciones ejecutadas por el dispositivo. Estas evaluaciones se traducen (gracias a los ErrPs) en retroalimentación para el dispositivo, el cual las usa en un contexto de aprendizaje por refuerzo para mejorar su comportamiento. Esta tesis demuestra por primera vez este paradigma BMI de enseñanza con doce sujetos en tres experimentos en bucle cerrado concluyendo con la operación de un manipulador robótico real. Como la mayoría de BMIs, el paradigma propuesto requiere una etapa de calibración específica para cada sujeto y tarea. Esta fase, un proceso que requiere mucho tiempo y extenuante para el usuario, dificulta la distribución de los BMIs a aplicaciones fuera del laboratorio. En el caso particular del paradigma propuesto, una fase de calibración para cada tarea es altamente impráctico ya que el tiempo necesario para esta fase se suma al tiempo de aprendizaje de la tarea, retrasando sustancialmente el control final del dispositivo. Así, sería conveniente poder entrenar clasificadores capaces de funcionar independientemente de la tarea de aprendizaje que se esté ejecutando. Esta tesis analiza desde un punto de vista electrofisiológico cómo los potenciales se ven afectados por diferentes tareas ejecutadas por el dispositivo, mostrando cambios principalmente en la latencia la señal; y estudia cómo transferir el clasificador entre tareas de dos maneras: primero, aplicando clasificadores adaptativos del estado del arte, y segundo corrigiendo la latencia entre las señales de dos tareas para poder generalizar entre ambas. Otro reto importante bajo este paradigma viene del tiempo necesario para aprender la tarea. Debido al bajo ratio de información transferida por minuto del BMI, el sistema tiene una pobre escalabilidad: el tiempo de aprendizaje crece exponencialmente con el tamaño del espacio de aprendizaje, y por tanto resulta impráctico obtener el comportamiento motor óptimo mediante aprendizaje por refuerzo. Sin embargo, este problema puede resolverse explotando la estructura de la tarea de aprendizaje. Por ejemplo, si el número de posiciones a alcanzar es discreto se puede pre-calcular la política óptima para cada posible posición. En esta tesis, se muestra cómo se puede usar la estructura de la tarea dentro del paradigma propuesto para reducir enormemente el tiempo de aprendizaje de la tarea (de diez minutos a apenas medio minuto), mejorando enormemente así la escalabilidad del sistema. Finalmente, esta tesis muestra cómo, gracias a las lecciones aprendidas en los descubrimientos anteriores, es posible eliminar completamente la etapa de calibración del paradigma propuesto mediante el aprendizaje no supervisado del clasificador al mismo tiempo que se está ejecutando la tarea. La idea fundamental es calcular un conjunto de clasificadores que sigan las restricciones de la tarea anteriormente usadas, para a continuación seleccionar el mejor clasificador del conjunto. De esta manera, esta tesis presenta un BMI plug-and-play que sigue el paradigma propuesto, aprende la tarea y el clasificador y finalmente alcanza la posición del espacio deseada por el usuario

Human inspired robotic path planning and heterogeneous robotic mapping

One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains

Led into Temptation? Rewarding Brand Logos Bias the Neural Encoding of Incidental Economic Decisions

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness

Brain mechanisms of affect and learning

Learning and affect are considered empirically separable, but these constructs bidirectionally interact. While it has been demonstrated that dopamine supports the informational component of reward learning, the term "reward" inherently infers that a subjective positive experience is necessary to drive appetitive behavior. In this dissertation, I will first review the ways in which dopamine operates on the levels of physiology and systems neuroscience to support learning from both positive and negative outcomes, as well as how this framework may be employed to study mechanism and disease. I will then review the ways in which learning may interact with or be supported by other brain systems, starting with affective networks and extending into systems that support memory and other types of broader decision making processes. Finally, my introduction will discuss a disease model, schizophrenia, and how applying questions pertaining to learning theory may contribute to understanding symptom-related mechanisms. The first study (Chapter 2) will address the way in which affective and sensory mechanisms may alter pain-related decisions. I will demonstrate that subjects will choose to experience a stimulus that incorporates a moment of pain relief over a shorter stimulus that encompasses less net pain, and will suggest that the positive prediction error associated with the pain relief may modulate explicit memory in such a way that impacts later decision making. In the second study (Chapter 3), I will examine reward learning in patients with schizophrenia, and demonstrate selective learning deficits from gains as opposed to losses, as well as relationships in performance to affective and motivational symptoms. The third study (Chapter 4) will extend this disease model to a novel cohort of subjects who perform the same reward learning task while undergoing functional MRI. The data from this chapter will reveal deficits in the patient group during choice in orbitofrontal cortex, as well as an abnormal pattern of learning signal responses during feedback versus outcome, particularly in orbitofrontal cortex, a finding that correlates with affective symptoms in medial PFC. Taken together, these data demonstrate that learning is comprised of both informational and affective processes that incorporate input from dopaminergic midbrain neurons and its targets, as well as integration from other affective, mnemonic, and sensory regions to support healthy learning, emotion, and adaptive behavior

XCS Performance and Population STRUCTURE IN MULTI-STEP ENVIRONMENTS

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN039134 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Un Mécanisme Constructiviste d'Apprentissage Automatique d'Anticipations pour des Agents Artificiels Situés

This research is characterized, first, by a theoretical discussion on the concept of autonomous agent, based on elements taken from the Situated AI and the Affective AI paradigms. Secondly, this thesis presents the problem of learning world models, providing a bibliographic review regarding some related works. From these discussions, the CAES architecture and the CALM mechanism are presented. The CAES (Coupled Agent-Environment System) is an architecture for describing systems based on the agent-environment dichotomy. It defines the agent and the environment as two partially open systems, in dynamic coupling. The agent is composed of two sub-systems, mind and body, following the principles of situativity and intrinsic motivation. CALM (Constructivist Learning Anticipatory Mechanism) is based on the constructivist approach to Artificial Intelligence. It allows a situated agent to build a model of the world in environments partially deterministic and partially observable in the form of Partially Observable and Factored Markov Decision Process (FPOMDP). The model of the world is constructed and used for the agent to define a policy for action in order to improve its own performance.Cette recherche se caractérise, premièrement, par une discussion théorique sur le concept d'agent autonome, basée sur des éléments issus des paradigmes de l'Intelligence Artificielle Située et de l'Intelligence Artificielle Affective. Ensuite, cette thèse présente le problème de l'apprentissage de modèles du monde, en passant en revue la littérature concernant les travaux qui s'y rapportent. À partir de ces discussions, l'architecture CAES et le mécanisme CALM sont présentés. CAES (Coupled Agent-Environment System) constitue une architecture pour décrire des systèmes basés sur la dichotomie agent-environnement. Il définit l'agent et l'environnement comme deux systèmes partiellement ouverts, en couplage dynamique. L'agent, à son tour, est composé de deux sous-systèmes, l'esprit et le corps, suivant les principes de la situativité et de la motivation intrinsèque. CALM (Constructivist Anticipatory Learning Mechanism) est un mécanisme d'apprentissage fondé sur l'approche constructiviste de l'Intelligence Artificielle. Il permet à un agent situé de construire un modèle du monde dans des environnements partiellement observables et partiellement déterministes, sous la forme d'un processus de décision markovien partiellement observable et factorisé (FPOMDP). Le modèle du monde construit est ensuite utilisé pour que l'agent puisse définir une politique d'action visant à améliorer sa propre performance