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Autonomous Mental Development, IEEE Transactions on

Issue 1 • Date March 2013

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  • Table of contents

    Publication Year: 2013 , Page(s): C1
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  • IEEE Transactions on Autonomous Mental Development publication information

    Publication Year: 2013 , Page(s): C2
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  • Editorial - TAMD Outstanding Paper Award and Open Access Publication Established

    Publication Year: 2013 , Page(s): 1
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  • Second Annual IEEE ICDL and EpiRob 2012: Conference Summary and Report

    Publication Year: 2013 , Page(s): 2
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  • The Coordinating Role of Language in Real-Time Multimodal Learning of Cooperative Tasks

    Publication Year: 2013 , Page(s): 3 - 17
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2477 KB) |  | HTML iconHTML  

    One of the defining characteristics of human cognition is our outstanding capacity to cooperate. A central requirement for cooperation is the ability to establish a “shared plan”-which defines the interlaced actions of the two cooperating agents-in real time, and even to negotiate this shared plan during its execution. In the current research we identify the requirements for cooperation, extending our earlier work in this area. These requirements include the ability to negotiate a shared plan using spoken language, to learn new component actions within that plan, based on visual observation and kinesthetic demonstration, and finally to coordinate all of these functions in real time. We present a cognitive system that implements these requirements, and demonstrate the system's ability to allow a Nao humanoid robot to learn a nontrivial cooperative task in real-time. We further provide a concrete demonstration of how the real-time learning capability can be easily deployed on a different platform, in this case the iCub humanoid. The results are considered in the context of how the development of language in the human infant provides a powerful lever in the development of cooperative plans from lower-level sensorimotor capabilities. View full abstract»

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  • A Survey of the Ontogeny of Tool Use: From Sensorimotor Experience to Planning

    Publication Year: 2013 , Page(s): 18 - 45
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1156 KB) |  | HTML iconHTML  

    In this paper, we review current knowledge on tool use development in infants in order to provide relevant information to cognitive developmental roboticists seeking to design artificial systems that develop tool use abilities. This information covers: 1) sketching developmental pathways leading to tool use competences; 2) the characterization of learning and test situations; 3) the crystallization of seven mechanisms underlying the developmental process; and 4) the formulation of a number of challenges and recommendations for designing artificial systems that exhibit tool use abilities in complex contexts. View full abstract»

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  • Learning Information Acquisition for Multitasking Scenarios in Dynamic Environments

    Publication Year: 2013 , Page(s): 46 - 61
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1904 KB) |  | HTML iconHTML  

    Real world environments are so dynamic and unpredictable that a goal-oriented autonomous system performing a set of tasks repeatedly never experiences the same situation even though the task routines are the same. Hence, manually designed solutions to execute such tasks are likely to fail due to such variations. Developmental approaches seek to solve this problem by implementing local learning mechanisms to the systems that can unfold capabilities to achieve a set of tasks through interactions with the environment. However, gathering all the information available in the environment for local learning mechanisms to process is hardly possible due to limited resources of the system. Thus, an information acquisition mechanism is necessary to find task-relevant information sources and applying a strategy to update the knowledge of the system about these sources efficiently in time. A modular systems approach may provide a useful structured and formalized basis for that. In such systems different modules may request access to the constrained system resources to acquire information they are tuned for. We propose a reward-based learning framework that achieves an efficient strategy for distributing the constrained system resources among modules to keep relevant environmental information up to date for higher level task learning and executing mechanisms in the system. We apply the proposed framework to a visual attention problem in a system using the iCub humanoid in simulation. View full abstract»

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  • A Spike-Based Model of Neuronal Intrinsic Plasticity

    Publication Year: 2013 , Page(s): 62 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2157 KB) |  | HTML iconHTML  

    The discovery of neuronal intrinsic plasticity (IP) processes which persistently modify a neuron's excitability necessitates a new concept of the neuronal plasticity mechanism and may profoundly influence our ideas on learning and memory. In this paper, we propose a spike-based IP model/adaptation rule for an integrate-and-fire (IF) neuron to model this biological phenomenon. By utilizing spikes denoted by Dirac delta functions rather than computing instantaneous firing rates for the time-dependent stimulus, this simple adaptation rule adjusts two parameters of an individual IF neuron to modify its excitability. As a result, this adaptation rule helps an IF neuron to keep its firing activity in a relatively “low but not too low” level and makes the spike-count distributions computed with adjusted window sizes similar to the experimental results. View full abstract»

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  • Autonomous and Interactive Improvement of Binocular Visual Depth Estimation through Sensorimotor Interaction

    Publication Year: 2013 , Page(s): 74 - 84
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1584 KB) |  | HTML iconHTML  

    We investigate how a humanoid robot with a randomly initialized binocular vision system can learn to improve judgments about egocentric distances using limited action and interaction that might be available to human infants. First, we show how distance estimation can be improved autonomously. We consider our approach to be autonomous because the robot learns to accurately estimate distance without a human teacher providing the distances to training targets. We find that actions that, in principle, do not alter the robot's distance to the target are a powerful tool for exposing estimation errors. These errors can be used to train a distance estimator. Furthermore, the simple action used (i.e., neck rotation) does not require high level cognitive processing or fine motor skill. Next, we investigate how interaction with humans can further improve visual distance estimates. We find that human interaction can improve distance estimates for far targets outside of the robot's peripersonal space. This is accomplished by extending our autonomous approach above to integrate additional information provided by a human. Together these experiments suggest that both action and interaction are important tools for improving perceptual estimates. View full abstract»

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  • Erratum to "Human-Recognizable Robotic Gestures" [Dec 12 305-314]

    Publication Year: 2013 , Page(s): 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (87 KB) |  | HTML iconHTML  

    In the above-named article [ibid., vol. 4, no. 4, pp. 305-314, Dec. 2012], the current affiliation within the biography of J.-J. Cabibihan was mistakenly written as Gemalto Singapore, Singapore. That is the current affiliation of S. Pramanik. Dr. Cabibihan's current affiliation is the National University of Singapore. View full abstract»

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  • IEEE Xplore Digital Library

    Publication Year: 2013 , Page(s): 86
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  • Quality without compromise

    Publication Year: 2013 , Page(s): 87
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  • Proven powerful

    Publication Year: 2013 , Page(s): 88
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2013 , Page(s): C3
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  • IEEE Transactions on Autonomous Mental Development information for authors

    Publication Year: 2013 , Page(s): C4
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Aims & Scope

IEEE Transactions on Autonomous Mental Development (TAMD) includes computational modeling of mental development, including mental architecture, theories, algorithms, properties, and experiments.

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Meet Our Editors

Editor-in-Chief
Zhengyou Zhang
Microsoft Research