Autonomous learning algorithm and associative memory for intelligent robots

Abstract

We propose autonomous learning algorithm based on the internal state of the associative memory for intelligent robots. The proposed associative memory model consists of structural unstable oscillators and a common field such as chemical concentration. In computer simulations, we use the binary pattern as the stimuli. When the pattern memorized in the network is given to the network from the outer world, the internal state of the network becomes a periodic state. On the other hand, when the pattern has not been memorized is given to the network, the state becomes an intermittently chaotic and the output of the network travels around the input and some memorized patterns. This chaotic state is regarded as "I don't know" state. Further, when the proposed autonomous learning algorithm is applied to the proposed network, the network can learn only the novel patterns automatically without destroying the previously memorized patterns.