By Topic

Autonomous Mental Development, IEEE Transactions on

Issue 3 • Date Oct. 2009

Filter Results

Displaying Results 1 - 16 of 16
  • Table of contents

    Publication Year: 2009 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (117 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Autonomous Mental Development publication information

    Publication Year: 2009 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (36 KB)  
    Freely Available from IEEE
  • Editorial

    Publication Year: 2009 , Page(s): 153
    Save to Project icon | Request Permissions | PDF file iconPDF (26 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Report on the IEEE 8th International Conference on Development and Learning (ICDL-2009)

    Publication Year: 2009 , Page(s): 154
    Save to Project icon | Request Permissions | PDF file iconPDF (26 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • R-IAC: Robust Intrinsically Motivated Exploration and Active Learning

    Publication Year: 2009 , Page(s): 155 - 169
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2331 KB) |  | HTML iconHTML  

    Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called robust intelligent adaptive curiosity (R-IAC), and show that its performances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Coevolution of Role-Based Cooperation in Multiagent Systems

    Publication Year: 2009 , Page(s): 170 - 186
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (874 KB) |  | HTML iconHTML  

    In tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called multiagent enforced subpopulations (multiagent ESP), is proposed and demonstrated in a prey-capture task. First, the approach is shown to be more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e., through role-based responses to the environment, rather than communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multiagent tasks. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • What is Needed for a Robot to Acquire Grammar? Some Underlying Primitive Mechanisms for the Synthesis of Linguistic Ability

    Publication Year: 2009 , Page(s): 187 - 195
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (467 KB) |  | HTML iconHTML  

    A robot that can communicate with humans using natural language will have to acquire a grammatical framework. This paper analyses some crucial underlying mechanisms that are needed in the construction of such a framework. The work is inspired by language acquisition in infants, but it also draws on the emergence of language in evolutionary time and in ontogenic (developmental) time. It focuses on issues arising from the use of real language with all its evolutionary baggage, in contrast to an artificial communication system, and describes approaches to addressing these issues. We can deconstruct grammar to derive underlying primitive mechanisms, including serial processing, segmentation, categorization, compositionality, and forward planning. Implementing these mechanisms are necessary preparatory steps to reconstruct a working syntactic/semantic/pragmatic processor which can handle real language. An overview is given of our own initial experiments in which a robot acquires some basic linguistic capacity via interacting with a human. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Dynamic Systems Model of Infant Attachment

    Publication Year: 2009 , Page(s): 196 - 207
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (215 KB) |  | HTML iconHTML  

    Attachment, or the emotional tie between an infant and its primary caregiver, has been modeled as a homeostatic process by Bowlby's (Attachment and Loss, 1969; Anxiety and Depression, 1973; Loss: Sadness and Depression, 1980). Evidence from neurophysiology has grounded such mechanism of infant attachment to the dynamic interplay between an opioid-based proximity-seeking mechanism and an NE-based arousal system that are regulated by external stimuli (interaction with primary caregiver and the environment). Here, we model such attachment mechanism and its dynamic regulation by a coupled system of ordinary differential equations. We simulated the characteristic patterns of infant behaviors in the strange situation procedure, a common instrument for assessing the quality of attachment outcomes (??types??) for infants at about one year of age. We also manipulated the parameters of our model to account for neurochemical adaptation, and to allow for caregiver style (such as responsiveness and other factors) and temperamental factor (such as reactivity and readiness in self-regulation) to be incorporated into the homeostatic regulation model of attachment dynamics. Principle component analysis revealed the characteristic regions in the parameter space that correspond to secure, anxious, and avoidant attachment typology. Implications from this kind of approach are discussed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    Publication Year: 2009 , Page(s): 208
    Save to Project icon | Request Permissions | PDF file iconPDF (755 KB)  
    Freely Available from IEEE
  • Special issue on active learning and intrinsically motivated exploration in robots

    Publication Year: 2009 , Page(s): 209 - 210
    Save to Project icon | Request Permissions | PDF file iconPDF (659 KB)  
    Freely Available from IEEE
  • Special issue on representations and architectures for cognitive systems

    Publication Year: 2009 , Page(s): 211 - 212
    Save to Project icon | Request Permissions | PDF file iconPDF (369 KB)  
    Freely Available from IEEE
  • White box nonlinear prediction models

    Publication Year: 2009 , Page(s): 213
    Save to Project icon | Request Permissions | PDF file iconPDF (151 KB)  
    Freely Available from IEEE
  • 9th International Conference on Development and Learning (ICDL)

    Publication Year: 2009 , Page(s): 214 - 215
    Save to Project icon | Request Permissions | PDF file iconPDF (164 KB)  
    Freely Available from IEEE
  • Quality without compromise [advertisement]

    Publication Year: 2009 , Page(s): 216
    Save to Project icon | Request Permissions | PDF file iconPDF (324 KB)  
    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Publication Year: 2009 , Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (37 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Autonomous Mental Development Information for authors

    Publication Year: 2009 , Page(s): C4
    Save to Project icon | Request Permissions | PDF file iconPDF (28 KB)  
    Freely Available from IEEE

Aims & Scope

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

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Zhengyou Zhang
Microsoft Research