A working memory model improves cognitive control in agents and robots

Abstract Cognition entails those mental processes enabling understanding the current situation through senses, experience, and thought, and supporting the acquisition of new knowledge. A fundamental contribution in cognition is offered by the working memory, that is a small, short-term memory containing and protecting from interference goal-relevant pieces of information. Grounding our work on biological and neuroscientific studies, we modeled and implemented working memory processes in a software model, IDRA-WM, that can simultaneously act as short-term memory and actions generator, thanks to the use of a reinforcement-driven mechanism for chunk selection. Moreover our system integrates the functions of the working memory with a basic action planner. We tested the model with robot relevant tasks to assess whether the proposed solution can learn to solve a problem on the basis of a delayed reward. The experimental results indicate that IDRA-WM is able to solve even those tasks that do not provide immediate reward after an action.