20,757 research outputs found

    The Problem of Mental Action

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    In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference

    Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

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    This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

    Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

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    While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

    Intrinsic Motivation Systems for Autonomous Mental Development

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    Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values

    Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

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    Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

    Apprentissage permanent par feedback endogène, application à un système robotique

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    Les applications robotiques sont liées à l'environnement sociotechnique dynamique dans lequel elles sont intégrées. Dans ce contexte, l'auto-adaptation est une préoccupation centrale et la conception d'applications intelligentes dans de tels environnements nécessite de les considérer comme des systèmes complexes. Le domaine de la robotique est très vaste. L'accent est mis sur les systèmes qui s'adaptent aux contraintes de leur environnement et non sur la mécanique ou le traitement du signal. À la lumière de ce contexte, l'objectif de cette thèse est la conception d'un mécanisme d'apprentissage capable d'apprendre de manière continue en utilisant des feedbacks endogènes (i.e. des interactions internes) dans des environnements sociotechniques dynamiques. Ce mécanisme d'apprentissage doit aussi vérifier plusieurs propriétés qui sont essentielles dans ce contexte comme : l'agnosticité, l'apprentissage tout au long de la vie, l'apprentissage en ligne, l'auto-observation, la généralisation des connaissances, le passage à l'échelle, la tolérance au volume de données et l'explicabilité. Les principales contributions consistent en la construction de l'apprentissage endogène par contextes et la conception du mécanisme d'apprentissage ELLSA pour Endogenous Lifelong Learner by Self-Adaptation. Le mécanisme d'apprentissage proposé est basé sur les systèmes multi-agents adaptatifs combinés à l'apprentissage endogène par contextes. La création de l'apprentissage endogène par contextes est motivée par la caractérisation d'imprécisions d'apprentissage qui sont détectées par des négociations locales entre agents. L'apprentissage endogène par contextes comprends aussi un mécanisme de génération de données artificielles pour améliorer les modèles d'apprentissage tout en réduisant la quantité nécessaire de données d'apprentissage. Dans un contexte d'apprentissage tout au long de la vie, ELLSA permet une mise à jour dynamique des modèles d'apprentissage. Il introduit des stratégies d'apprentissage actif et d'auto-apprentissage pour résoudre les imprécisions d'apprentissage. L'utilisation de ces stratégies dépend de la disponibilité des données d'apprentissage. Afin d'évaluer ses contributions, ce mécanisme est appliqué à l'apprentissage de fonctions mathématiques et à un problème réel dans le domaine de la robotique : le problème de la cinématique inverse. Le scénario d'application est l'apprentissage du contrôle de bras robotiques multi-articulés. Les expériences menées montrent que l'apprentissage endogène par contextes permet d'améliorer les performances d'apprentissage grâce à des mécanismes internes. Elles mettent aussi en évidence des propriétés du système selon les objectifs de la thèse : feedback endogènes, agnosticité, apprentissage tout au long de la vie, apprentissage en ligne, auto-observation, généralisation, passage à l'échelle, tolérance au volume de données et explicabilité.Robotic applications are linked to the dynamic sociotechnical environment in which they are embedded. In this scope, self-adaptation is a central concern and the design of intelligent applications in such environments requires to consider them as complex systems. The field of robotics is very broad. The focus is made on systems that adapt to the constraints of their environment and not on mechanics or signal processing. In light of this context, the objective of this thesis is the design of a learning mechanism capable of continuous learning using endogenous feedback (i.e. internal interactions) in dynamic sociotechnical environments. This learning mechanism must also verify several properties that are essential in this context such as: agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability. The main contributions consist of the construction of Endogenous Context Learning and the design of the learning mechanism ELLSA for Endogenous Lifelong Learner by Self-Adaptation. The proposed learning mechanism is based on Adaptive Multi-Agent Systems combined with Context Learning. The creation of Endogenous Context Learning is motivated by the characterization of learning inaccuracies that are detected by local negotiations between agents. Endogenous Context Learning also includes an artificial data generation mechanism to improve learning models while reducing the amount of the required learning data. In a Lifelong Learning setting, ELLSA enables dynamic updating of learning models. It introduces Active Learning and Self-Learning strategies to resolve learning inaccuracies. The use of these strategies depends on the availability of learning data. In order to evaluate its contributions, this mechanism is applied to the learning of mathematical functions and to a real problem in the field of robotics: the Inverse Kinematics problem. The application scenario is the learning of the control of multi-jointed robotic arms. The conducted experiments show that Endogenous Context Learning enables to improve the learning performances thanks to internal mechanisms. They also highlight the properties of the system according to the objectives of the thesis: endogenous feedback, agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability
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