On reducing learning time in context-dependent mappings

An approach to overcoming the slow convergence problems often associated with learning complex nonlinear mappings is presented. The mappings are learned in a context-dependent manner so that complex problems are decomposed into simpler subproblems corresponding to different contexts. While no general conditions for determining applicability the method have been found, its power is illustrated through experiments in controlling simulated robot manipulators in two and three degrees of freedom. The experiments also indicate that the method shows promising scale-up properties