title: Incremental acquisition of multiple nonlinear forward models based on differentiation process of schema model.
abstract: We introduce the schema model as an alternative computational model representing multiple internal models. The human 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.
authors.affiliation: Graduate School of Informatics, Kyoto University, Japan.
authors.forename: Tadahiro
authors.initials: T
authors.lastname: Taniguchi
authors.forename: Tetsuo
authors.initials: T
authors.lastname: Sawaragi
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D013997:Time Factors