Realtime Task-Switching Driven by Value Evaluation with Associative Memory

Abstract

The ability to change behavior depending situation of a dynamic environment is an essential ability of animals for survival. However, conventional behavior learning algorithms can learn just in a static environment. So, in this paper, we propose a cognitive architecture that switches its conducting task in real time depending on its environmental situation. The architecture is realized by the combination of a value- based task selection and a parallel environmental situation prediction with an associative memory.