A neural network endowed with symbolic processing ability

We propose a neural network method for the generation of symbolic expressions using reinforcement learning. According to the proposed method, a human decides on the kind and number of primitive functions which, with the appropriate composition (in the mathematical sense), can represent a mapping between two domains. The appropriate composition is achieved by an agent which tries many compositions and receives a reward depending on the quality of the composed function.