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Journal article
Fast, slow, and metacognitive thinking in AI
npj Artificial Intelligence, Vol.1, 27
10/01/2025
Abstract
Inspired by the ”thinking fast and slow” cognitive theory of human decision making, we propose a multi-agent cognitive architecture (SOFAI) that is based on ”fast”/”slow” solvers and a metacognitive module. 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 that combining the two decision modalities through a separate metacognitive function allows for higher decision quality with less resource consumption compared to employing only one of the two modalities. Analyzing how the system achieves this, we also provide evidence for the emergence of several human-like behaviors, including skill learning, adaptability, and cognitive control.
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- Title
- Fast, slow, and metacognitive thinking in AI
- Publication Details
- npj Artificial Intelligence, Vol.1, 27
- Resource Type
- Journal article
- Publisher
- Nature Publishing Group UK
- Grant note
- IIS-RI-2007955, IIS-III-2107505, IIS-RI-2134857, IIS-RI-2339880 and CNS-SCC-2427237 / NSF Awards
- Identifiers
- 99381516564706600
- Academic Unit
- Institute for Human and Machine Cognition; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English