Teleoreactive Neural Networks

机译：立体神经网络

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The Teleo-Reactive (TR) formalism has been presented as a new programming paradigm to write reactive robot control programs. The formalism is based in a circuit semantics that intuitively can be proted in a direct way to a layered neural network architecture. But to capture the essence of the TR programs a more sophisticated mechanism of synthesis must be developed, that allows to express in a neural architecture 1) the reactive nature of the programs, 2) the incremental learning or new TR sequences and trees and 3) the continuous feedback from the world. We present an analysis of TR programs and a method to synthesize those programs into an ontogenic neural network model that captures all the features of the program and can evolve with the agent as he explore the world. This method can be easily integrated to the learning architecture of the reactive agent.

机译：Tree-Reactive（TR）形式主义已作为一种新的编程范例，以编写反应机器人控制程序。形式主义基于电路语义，可直观地以直接的方式被保护到分层神经网络架构。但要捕捉TR节目的本质，必须开发更复杂的合成机制，允许在神经结构中表达1）程序的反应性，2）增量学习或新的TR序列和树木和3）来自世界的持续反馈。我们对TR节目的分析和一种方法将这些程序综合到捕获程序的一个植入神经网络模型中，可以随着他探索世界而与代理商发展。该方法可以很容易地集成到反应剂的学习架构。

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《International work-conference on artificial and natural neural networks》|1997年||共10页
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Jose Ramirez;
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中图分类神经生理学;
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Teleoreactive Neural Networks

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