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Current AI systems lack several important human capabilities, such asadaptability, generalizability, self-control, consistency, common sense, andcausal reasoning. We believe that existing cognitive theories of human decisionmaking, such as the thinking fast and slow theory, can provide insights on howto advance AI systems towards some of these capabilities. In this paper, wepropose a general architecture that is based on fast/slow solvers and ametacognitive component. We then present experimental results on the behaviorof an instance of this architecture, for AI systems that make decisions aboutnavigating in a constrained environment. We show how combining the fast andslow decision modalities allows the system to evolve over time and graduallypass from slow to fast thinking with enough experience, and that this greatlyhelps in decision quality, resource consumption, and efficiency.

Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments