Knowledge of Knowledge and Intelligent Experimentation for Learning Control
Conference Paper, Proceedings of Seattle International Joint Conference on Neural Networks, Vol. 2, pp. 683 - 688, July, 1991
Abstract
It is shown that if a learning system is able to provide some estimate of the reliability of the generalizations it produces, then the rate of learning can be considerably increased. The increase is achieved by a decision-theoretic estimate of the value of trying alternative experimental actions. A further consequence of this kind of learning is that experience becomes concentrated in regions of the control space which are relevant to the task at hand. Such a restriction of experience is essential for continuous multivariate control tasks because the entire state space of such tasks could not possibly be learned in a practical amount of time.
BibTeX
@conference{Moore-1991-13275,author = {Andrew Moore},
title = {Knowledge of Knowledge and Intelligent Experimentation for Learning Control},
booktitle = {Proceedings of Seattle International Joint Conference on Neural Networks},
year = {1991},
month = {July},
volume = {2},
pages = {683 - 688},
}
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