Strategy Learning with Multilayer Connectionist Representations 1

Results are presented that demonstrate the learning and ne-tuning of search strategies using connectionist mechanisms. Previous studies of strategy learning within the symbolic, production-rule formalism have not addressed ne-tuning behavior. Here a two-layer connectionist system is presented that develops its search from a weak to a task-speciic strategy and ne-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.