Acquisition of multiple nonlinear internal models based on schema model

Abstract

We introduce the schema model as an alternative computational model representing multiple internal models. The humna central nervous system is believed to obtain multiple forward-inverse models. The schema model enables agents to obtain multiple nonlinear forward models incrementally. This model is based on hypothesis testing theory whereas most modular learning methods are based on a Bayesian framework. As a specific example, we describe a schema model with a normalized Gaussian network (NGSM). Simulation revealed that NGSM has two advantages over MOSAIC's learning method: NGSM can obtain multiple models incrementally and does not depend on the initial parameters of the forward models.