The Hybrid Integration of Perceptual Symbol Systems and Interactive Reinforcement Learning

Abstract

In order to produce robots which can interact more effectively with humans we propose that it is necessary for their cognitive processes to be grounded in the same perceptual elements as humans deal with. Perceptual symbol systems offer an attractive mechanism for capturing the symbolic properties of the senses and for integrating them into higher level cognitive processes. We have designed a perceptual symbol system where the robot learns about objects through interaction and reinforcement and have carried out experiments to assess the merits of this approach. We show that the use of human perceptual elements combined with interactive reinforcement leads to intuitive learning and interpretable knowledge structures.