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Science In Science, Vol. 318, No. 5850. (26 October 2007), pp. 594-598.
Abstract
The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly ...
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New Mathematics and Natural Computation (NMNC), Vol. 5, No. 01. (2009), pp. 307-334.
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Neural Netw., Vol. 18, No. 1. (2005), pp. 103-104.
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From Animals to Animats 10 (2008), pp. 331-341.
Abstract
We propose a sub-symbolic connectionist model in which a functionally compositional system self-organizes by learning a provided set of goal-directed actions. This approach is compatible with an idea taken from usage-based accounts of the developmental learning of language, especially one theory of infants’ acquisition process of symbols. The presented model potentially explains a possible continuous process underlying the transitions from rote knowledge to systematized knowledge by drawing an analogy to the formation process of a geometric regular arrangement of points. Based ...
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PLoS computational biology, Vol. 4, No. 11. (7 November 2008), e1000220.
Abstract
It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use ...
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In AAMAS '03: Proceedings of the second international joint conference on Autonomous agents and multiagent systems (2003), pp. 241-248.
Abstract
Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach ...
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International Journal of Humanoid Robotics, Vol. 1, No. 2. (2004), pp. 237-288.
Abstract
Control for and interaction with humanoid robots is often restrictive due to limitations of the robot platform and the high dimensionality of controlling systems with many degrees of freedom. We focus on the problem of providing a "skill-level interface" for a humanoid robot. Such an interface serves as (i) a modular foundation for structuring task-oriented control, (ii) a parsimonious abstraction of motor-level control (e.g. PD-servo control), and (iii) a means for grounding interactions between humans and robots through common skill vocabularies. ...
Note (first note only)
http://scholar.google.co.jp/scholar.bib?hl=ja&lr=&output=search&q=info:S6hOmGJw_mEJ:scholar.google.com/&output=citation&oe=SJS&oi=citation
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In AAAI Proceedings, AAAI Spring Symposium To Boldy Go Where No Human-Robot Team Has Gone Before (2006)
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Robotics and Autonomous Systems, Vol. 47, No. 2-3. (June 2004), pp. 69-77.
Abstract
This paper develops a general policy for learning the relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies ...
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Journal of Consciousness Studies, Vol. 5 (1998), pp. 516-542.
Abstract
This study attempts to describe the notion of the "self " using dynamical systems language based on the results of our robot learning experiments. A neural network model consisting of multiple modules is proposed, in which the interactive dynamics between the bottom-up perception and the top-down prediction are investigated. Our experiments with a real mobile robot showed that the incremental learning of the robot switches spontaneously between steady and unsteady phases. In the steady phase, the top-down prediction for the bottom-up ...
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