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Computational Intelligence Magazine, IEEE

Issue 3 • Date Aug. 2006

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Displaying Results 1 - 18 of 18
  • Computational Maps in the Visual Cortex [Book Review]

    Page(s): 54 - 55
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  • CIS: Conference activities

    Page(s): 2 - 3
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  • IEEE Computational Intelligence Magazine - Aug. 2006

    Page(s): 0_1
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    Freely Available from IEEE
  • IEEE Symposium Series on Computational Intelligence - Call for Papers

    Page(s): 0_2
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    Freely Available from IEEE
  • Table of contents - Vol 1 No 3

    Page(s): 1
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  • Building blocks of development [robot learning]

    Page(s): 4 - 8
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  • Collective intelligence

    Page(s): 9 - 12
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  • Family corner [IEEE Ukraine Section - Instrumentation & Measurement/Computational Intelligence Joint Societies Chapter (USI&M/CIJSC) was created June 7, 2005]

    Page(s): 14
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  • From neural networks to the brain: autonomous mental development

    Page(s): 15 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1620 KB) |  | HTML iconHTML  

    Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: vision-guided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, tour tasks that infants learn to perform early but very perceptually challenging for robots View full abstract»

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  • How computational models help explain the origins of reasoning

    Page(s): 32 - 40
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    Developmental psychology is ready to blossom into a modern science that focuses on causal mechanistic explanations of development rather than just describing and classifying the skills that children show at different ages. Computational models of cognitive development are formal systems that track the changes in information processing taking place as a behavior is acquired. Models are generally implemented as psychologically constrained computer simulations that learn tasks such as reasoning, categorization, and language. Their principal use is as tools for exploring mechanisms of transition (development) from one level of competence to the next during the course of cognitive development. They have been used to probe questions such as the extent of 'pre-programmed' or innate knowledge that exists in the infant mind, and how the sophistication of reasoning can increase with age and experience View full abstract»

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  • Social development [robots]

    Page(s): 41 - 47
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    Most robots are designed to operate in environments that are either highly constrained (as is the case in an assembly line) or extremely hazardous (such as the surface of Mars). Machine learning has been an effective tool in both of these environments by augmenting the flexibility and reliability of robotic systems, but this is often a very difficult problem because the complexity of learning in the real world introduces very high dimensional state spaces and applies severe penalties for mistakes. Human children are raised in environments that are just as complex (or even more so) than those typically studied in robot learning scenarios. However, the presence of parents and other caregivers radically changes the type of learning that is possible. Consciously and unconsciously, adults tailor their action and the environment to the child. They draw attention to important aspects of a task, help in identifying the cause of errors and generally tailor the task to the child's capabilities. Our research group builds robots that learn in the same type of supportive environment that human children have and develop skills incrementally through their interactions. Our robots interact socially with human adults using the same natural conventions that a human child would use. Our work sits at the intersection of the fields of social robotics (Fong et al., 2003; Breazeal and Scawellan, 2002) and autonomous mental development (Weng et al., 2000). Together, these two fields offer the vision of a machine that can learn incrementally, directly from humans, in the same ways that humans learn from each other. In this article, we introduce some of the challenges, goals, and applications of this research View full abstract»

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  • The Mental Development Repository

    Page(s): 48 - 49
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  • Autonomous mental development: soaring beyond tradition

    Page(s): 50 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (644 KB) |  | HTML iconHTML  

    This article provides a brief overview of the history of autonomous mental development as a discipline View full abstract»

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  • Conference calendar

    Page(s): 56
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  • 2006 IEEE Antennas and Propagation Society - Membership application

    Page(s): 03
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  • IEEE Enterprise [advertisement]

    Page(s): 04
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope