Current AI systems lack several important human capabilities, such as
adaptability, generalizability, self-control, consistency, common sense, and
causal reasoning. We believe that existing cognitive theories of human decision
making, such as the thinking fast and slow theory, can provide insights on how
to advance AI systems towards some of these capabilities. In this paper, we
propose a general architecture that is based on fast/slow solvers and a
metacognitive component. We then present experimental results on the behavior
of an instance of this architecture, for AI systems that make decisions about
navigating in a constrained environment. We show how combining the fast and
slow decision modalities allows the system to evolve over time and gradually
pass from slow to fast thinking with enough experience, and that this greatly
helps in decision quality, resource consumption, and efficiency.
