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

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

URL: http://arxiv.org/abs/2201.07050v1
Date: Tue, 18 Jan 2022 15:24:03 GMT
Title: Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Authors: Marianna B. Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi, Biplav Srivastava, Brent Venable
Abstract summary: 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.
Score: 43.178630971993286
License: http://creativecommons.org/licenses/by/4.0/
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 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.