Thinking Fast and Slow in AI: The Role of Metacognition

Researchers propose a multi-agent AI architecture inspired by Kahneman's "fast and slow thinking" theory, where "fast" agents make quick decisions based on past experience while "slow" agents engage in deliberate reasoning for complex problems. The approach aims to address limitations in current narrow AI systems by incorporating metacognitive capabilities—self-awareness about past actions and skills—supported by models of the world and self. This framework seeks to equip AI systems with human-like intelligence capabilities that go beyond pattern recognition and require reasoning and adaptive problem-solving.