The Inner-class Model as a Neural Network – An Experiment Supposing Sound or Language

Abstract

The inner class model, a model of general artificial intelligence, is hereby defined as a type of recurrent neural network. Although the inner class model is a type of reinforcement learning, its learning does not depend on rewards, but depends on how often the strengthening of knowledge is interfered by noise. Here, to strengthen the knowledge is to repeat the time-series episode after the episode took place with action. In addition, more noise supposes to be input from outside when an action fails than when it succeeds, and it referred to as "noise hypothesis." In the new definition, generating an inner class is equivalent to form a node cluster, and node clusters are formed by slightly increasing the weight of edges every time an edge is propagated. It was confirmed by a simple time series episode and by a small-scale RNN that node clusters are created by this new definition and that it is possible to repeat the episode.