Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
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.
Cite
@article{arxiv.2201.07050,
title = {Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments},
author = {Marianna B. Ganapini and Murray Campbell and Francesco Fabiano and Lior Horesh and Jon Lenchner and Andrea Loreggia and Nicholas Mattei and Taher Rahgooy and Francesca Rossi and Biplav Srivastava and Brent Venable},
journal= {arXiv preprint arXiv:2201.07050},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2110.01834