A Procedural Long Term Memory for Cognitive Robotics Optimizing Adaptive Learning in Dynamic Environments
Rodrigo Salgado, Francisco Bellas, Pilar Rivera Caamaño, B. Santos-Díez, Richard José Duro · 2012
This paper provides some insights into the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) cognitive architecture. The memory system is based on learning error stability and instability to detect if a model is candidate to enter the LTM or to be recovered. A LTM replacement strategy has been developed that is based on context detection using functional comparison of the models’ response. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results. Keywords-Adaptive Learning, Cognitive Robotics, Evolutionary Computation, Dynamic Environments