Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum development of concepts and related skills, the machine operates in the protective environment that gradually increases its complexity. Simulation illustrates the advantage of this gradual increase in environment complexity for machine development. Comparison to reinforcement learning indicates weakness of the later method in learning proper behavior, even in such protective environments with gradually increasing complexity. The method shows how mental development stimulates learning of new concepts and at the same time benefits from this learning. Thus the method addresses a well know problem of merging connectionist (bottom-up) and symbolic (top down) approaches for intelligent autonomous machine operation in developmental robotics.
['Janusz A. Starzyk', 'Pawel Raif', 'Ah-Hwee Tan']
Mental development and representation building through motivated learning