Abstract: 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 talk, I will describe a general architecture that is based on fast/slow solvers and a metacognitive component. I will 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. I will show how combining the fast and slow decision modalities allows this 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.