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Carnegie Mellon Robotics Institute

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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.

Notes

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Text Reference

Andrew Moore, "Knowledge of Knowledge and Intelligent Experimentation for Learning Control," Proceedings of the 1991 Seattle International Joint Conference on Neural Networks, July, 1991, pp. 683 - 688.

BibTeX Reference

@inproceedings{Moore_1991_2155,
   author = "Andrew Moore",
   title = "Knowledge of Knowledge and Intelligent Experimentation for Learning Control",
   booktitle = "Proceedings of the 1991 Seattle International Joint Conference on Neural Networks",
   pages = "683 - 688",
   month = "July",
   year = "1991",
   volume = "2",
}

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