cwa0@gte-labs.CSNET (Chuck Anderson) (07/13/87)
Strategy Learning with Multilayer Connectionist Representations
Chuck Anderson
(cwa@gte-labs.csnet)
GTE Laboratories Incorporated
40 Sylvan Road
Waltham, MA 02254
Abstract
Results are presented that demonstrate the learning and
fine-tuning of search strategies using connectionist mechanisms.
Previous studies of strategy learning within the symbolic,
production-rule formalism have not addressed fine-tuning behavior.
Here a two-layer connectionist system is presented that develops its
search from a weak to a task-specific strategy and fine-tunes its
performance. The system is applied to a simulated, real-time,
balance-control task. We compare the performance of one-layer and
two-layer networks, showing that the ability of the two-layer network
to discover new features and thus enhance the original representation
is critical to solving the balancing task.
(Also appears in the Proceedings of the Fourth International Workshop on
Machine Learning, Irvine, June, 1987)