Creation by refinement: a creativity paradigm for gradient descent learning networks

The author describes a paradigm for creating novel examples from the class of patterns recognized by a trained gradient-descent associative learning network. The paradigm consists of a learning phase, in which the network learns to identify patterns of the desired class, followed by a simple synthesis algorithm, in which a haphazard 'creation' is refined by a gradient-descent search complementary to the one used in learning. This paradigm is an alternative to one in which novel patterns are obtained by applying novel inputs to a learned mapping, and can be used for creative problems, such as music composition, which are not described by an input-output mapping. A simple simulation is shown in which a back-propagation network learns to judge simple patterns representing musical motifs, and then creates similar motifs.>