| Popis: |
Autonomous learning is the ability to learn effectively without much external assistance. An important strength of autonomous learners is that they can shape their own learning and development, in large part by choosing which problems to learn. Such choices include selecting a problem to learn and deciding whether to continue learning on that selected task or abandon it in favor of something else. We extend a constructive neurallearning algorithm, sibling-descendant cascade-correlation, to monitor lack of progress in learning so that unproductive learning can be abandoned. Learning is abandoned when network error fails to change by more than a specified threshold for a specified number of consecutive learning cycles. Here we explore the space defined by these threshold and patience parameters on problems of different degrees of learnability. Our results simulate findings from recent experiments with infants who abandon learning on difficult tasks and focus their attention on tasks of moderate difficulty. |
