Learning to Perceive the World as Articulated: An Approach for Hierarchical Learning in Sensory-Motor Systems (1999)
| Venue: | NEURAL NETWORKS |
| Citations: | 82 - 24 self |
BibTeX
@INPROCEEDINGS{Tani99learningto,
author = {Jun Tani and Stefano Nolfi},
title = {Learning to Perceive the World as Articulated: An Approach for Hierarchical Learning in Sensory-Motor Systems},
booktitle = {NEURAL NETWORKS},
year = {1999},
pages = {1131--1141},
publisher = {MIT Press}
}
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Abstract
This paper describes how agents can learn an internal model of the world structurally by focusing on the problem of behavior-based articulation. We develop an on-line learning scheme -- the so-called mixture of recurrent neural net (RNN) experts -- in which a set of RNN modules becomes self-organized as experts on multiple levels in order to account for the different categories of sensory-motor flow which the robot experiences. Autonomous switching of activated modules in the lower level actually represents the articulation of the sensory-motor flow. In the meanwhile, a set of RNNs in the higher level competes to learn the sequences of module switching in the lower level, by which articulation at a further more abstract level can be achieved. The proposed scheme was examined through simulation experiments involving the navigation learning problem. Our dynamical systems analysis clarified the mechanism of the articulation; the possible correspondence between the articulation...
