Thinking Fast and Slow in Human and Machine Intelligence

When working to build machines that have a form of intelligence, it is natural to be inspired by human intelligence. Of course, humans are very different from machines, in their embodiment and myriad other ways. Humans exploit their bodies to experience the world, create an internal model of it, and use this model to reason, learn, and make contextual and informed decisions. Machines lack the same embodiment, but often have access to both more memory and more computing power. Despite these crucial disanalogies, it is still useful to leverage our knowledge of how the human mind reasons and makes decisions to design and build machines that demonstrate behaviors similar to that of a human. In this article, we present a novel AI architecture, Slow and Fast AI (SOFAI), that is inspired by the "thinking fast and slow"cognitive theory of human decision making. SOFAI is a multi-agent architecture that employs both "fast"and "slow"solvers underneath a metacognitive agent that is able to both choose among a set of solvers as well as reflect on and learn from past experience. Experimental results on the behavior of two instances of the SOFAI architecture show that, compared to using just one of the two decision modalities, SOFAI is markedly better in terms of decision quality, resource consumption, and efficiency.