AI Needs Metacognition to Master Both Fast and Slow Thinking
Thinking Fast and Slow in AI: The Role of Metacognition
Despite rapid advances, AI remains narrow, lacking capabilities that come naturally to human intelligence. This paper proposes a multi-agent architecture inspired by Daniel Kahneman's theory of thinking fast and slow: incoming problems are solved either by system 1 agents that react using past experience, or by system 2 agents deliberately activated when reasoning and search for optimal solutions are required. Both agent types draw on a world model with domain knowledge and a self model tracking past actions and solver skills.
State-of-the-art AI still lacks many capabilities that would naturally be included in a notion of (human) intelligence.