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Title: Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments
Current AI systems lack several important human capabilities, such as adaptability, generalizability, selfcontrol, 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, which can be implemented by learning and reasoning components respectively, 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.  more » « less
Award ID(s):
2007955
PAR ID:
10386117
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) 2022
Page Range / eLocation ID:
171-185
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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