Date of Original Version
Abstract or Description
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
Proceedings of the 1991 Seattle International Joint Conference on Neural Networks, 683-688.