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Volume: 2 Issue: 4 Date: December 2010
(Previous issue:Vol. 2, No. 3, September 2010)
Abstract:The seven papers in this special issue focus on the development of artificial cognitive systems.
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Abstract:In this work, we present a novel probabilistic appearance representation and describe its application to surprise detection in the context of cognitive mobile robots. The luminance and chrominance of the environment are modeled by Gaussian distributions which are determined from the robot's observations using Bayesian inference. The parameters of the prior distributions over the mean and the precision of the Gaussian models are stored at a dense series of viewpoints along the robot's trajectory. Our probabilistic representation provides us with the expected appearance of the environment and enables the robot to reason about the uncertainty of the perceived luminance and chrominance. Hence, our representation provides a framework for the detection of surprising events, which facilitates attentional selection. In our experiments, we compare the proposed approach with surprise detection based on image differencing. We show that our surprise measure is a superior detector for novelty estimation compared to the measure provided by image differencing.
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Abstract: There are many different approaches to building a system that can engage in autonomous mental development. In this paper, we present an approach based on what we term self-understanding, by which we mean the explicit representation of and reasoning about what a system does and does not know, and how that knowledge changes under action. We present an architecture and a set of representations used in two robot systems that exhibit a limited degree of autonomous mental development, which we term self-extension. The contributions include: representations of gaps and uncertainty for specific kinds of knowledge, and a goal management and planning system for setting and achieving learning goals.
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Abstract: How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.
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Abstract: In this paper, we discuss the requirements of cognitive architectures for epigenetic robotics, and highlight the wider role that they can play in the development of the cognitive sciences. We discuss the ambitious goals of ongoing development, scalability, concept use and transparency, and introduce the epigenetic robotics architecture (ERA) as a framework guiding modeling efforts. A formal implementation is provided, demonstrated, and discussed in terms of meeting these goals. Extensions of the architecture are also introduced and we show how the dynamics of resulting models can transparently account for a wide range of psychological phenomena, without task dependant tuning, thereby making progress in all of the goal areas we highlight.
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Abstract: The multilevel Darwinist brain (MDB) is a cognitive architecture that follows an evolutionary approach to provide autonomous robots with lifelong adaptation. It has been tested in real robot on-line learning scenarios obtaining successful results that reinforce the evolutionary principles that constitute the main original contribution of the MDB. This preliminary work has lead to a series of improvements in the computational implementation of the architecture so as to achieve realistic operation in real time, which was the biggest problem of the approach due to the high computational cost induced by the evolutionary algorithms that make up the MDB core. The current implementation of the architecture is able to provide an autonomous robot with real time learning capabilities and the capability for continuously adapting to changing circumstances in its world, both internal and external, with minimal intervention of the designer. This paper aims at providing an overview or the architecture and its operation and defining what is required in the path towards a real cognitive robot following a developmental strategy. The design, implementation and basic operation of the MDB cognitive architecture are presented through some successful real robot learning examples to illustrate the validity of this evolutionary approach.
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Abstract: Inspired by child development and brain research, we introduce a computational framework which integrates robotic active vision and reaching. Essential elements of this framework are sensorimotor mappings that link three different computational domains relating to visual data, gaze control, and reaching. The domain of gaze control is the central computational substrate that provides, first, a systematic visual search and, second, the transformation of visual data into coordinates for potential reach actions. In this respect, the representation of object locations emerges from the combination of sensorimotor mappings. The framework is tested in the form of two different architectures that perform visually guided reaching. Systematic experiments demonstrate how visual search influences reaching accuracy. The results of these experiments are discussed with respect to providing a reference architecture for developmental learning in humanoid robot systems.
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Abstract: We describe a bootstrapping cognitive robot system that-mainly based on pure exploration-acquires rich object representations and associated object-specific grasp affordances. Such bootstrapping becomes possible by combining innate competences and behaviors by which the system gradually enriches its internal representations, and thereby develops an increasingly mature interpretation of the world and its ability to act within it. We compare the system's prior competences and developmental progress with human innate competences and developmental stages of infants.
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Abstract:Lists the reviewers who contributed to IEEE Transactions on Autonomous Mental Development for 2010.
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