Machine acquisition of skills by neural networks

Presents a theory and an architecture on machine skill acquisition and its implementation in neural networks. Particular emphasis is given to the skill acquisition in man/machine systems where the neural network observes control behavior of a human expert and learns rules behind his expertise. The paradigm of machine acquisition of skills implies the machine exploitation of its own skills through the exploration of experience based on the transferred skills. A new neural network architecture and a learning algorithm, referred to as the hierarchically self-organizing learning (HSOL) network, is presented especially for skill learning. The HSOL network is a dynamically competitive or cooperative network with the ability of self-organizing hidden units, and functions as a universal approximator of arbitrary input-output mappings. This network is applied to machine acquisition of game playing skills, and its performance is compared with that of other networks, including backpropagation, fully connected network, bidirectional associative memory, and recurrent network