Thinking Fast and Slow in AI: the Role of Metacognition

Abstract: AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life. However, we are still mostly seeing instances of narrow AI: many of these recent developments are typically focused on a very limited set of competencies and goals, e.g., image interpretation, natural language processing, classification, prediction, and many others. Moreover, while these successes can be accredited to improved algorithms and techniques, they are also tightly linked to the availability of huge datasets and computational power. State-of-the-art AI still lacks many capabilities that would naturally be included in a notion of (human) intelligence. We argue that a better study of the mechanisms that allow humans to have these capabilities can help us understand how to imbue AI systems with these competencies. We focus especially on D. Kahneman's theory of thinking fast and slow, and we propose a multi-agent AI architecture where incoming problems are solved by either system 1 (or "fast") agents, that react by exploiting only past experience, or by system 2 (or "slow") agents, that are deliberately activated when there is the need to reason and search for optimal solutions beyond what is expected from the system 1 agent. Both kinds of agents are supported by a model of the world, containing domain knowledge about the environment, and a model of "self", containing information about past actions of the system and solvers' skills.

• The paper presents the SOFAI architecture that integrates fast-thinking (system 1) and slow-thinking (system 2) agents, orchestrated by a meta-cognitive module for enhanced decision-making.
• It employs a dual-phase meta-cognitive module (MC1 and MC2) to rapidly evaluate solutions and determine when deeper, resource-intensive analysis is necessary.
• Practical implementations in trajectory and epistemic planning demonstrate the architecture's flexibility and potential to advance general AI capabilities.

Thinking Fast and Slow in AI: the Role of Metacognition

The paper "Thinking Fast and Slow in AI: the Role of Metacognition" (2110.01834) proposes an innovative approach to enhancing AI systems using insights derived from human cognition theories. Specifically, it leverages Daniel Kahneman's "Thinking, Fast and Slow" framework to create a multi-agent AI architecture termed SOFAI (SlOw and Fast AI). This architecture aims to emulate human decision-making processes by incorporating separate "fast" and "slow" thinking agents, equivalent to Kahneman's systems.

Advances in AI and the SOFAI Architecture

Recent years have witnessed significant advances in AI, however, most developments remain narrowly focused, lacking the general intelligence attributes observed in humans. To address these limitations, the SOFAI architecture employs fast-thinking agents (system 1) for intuitive tasks, and slow-thinking agents (system 2) for complex and rational problems; a meta-cognitive agent orchestrates between them based on resource availability, past experiences, and expected outcomes.

In practice, the system 1 agents apply past experience for immediate problem-solving, functioning within constant time complexity irrespective of problem scale. These agents are supported by a model that maintains domain knowledge and the agents' historical performance. When tasks transcend the capabilities of system 1 agents, system 2 agents are activated to undertake rigorous reasoning processes. The meta-cognitive agent plays a critical role in evaluating whether to use system 1 solutions directly or to invoke system 2, thereby optimizing resource use and decision quality.

Meta-cognition in AI: Monitoring and Control

Meta-cognition is central to the SOFAI architecture, embodying processes that oversee and regulate cognitive activities, enhancing decision quality. This paper proposes a centralized meta-cognitive module that arbitrates between system 1 and system 2 agents. It distinguishes itself by integrating introspective assessments and employing algorithm portfolio selection strategies to enhance solver efficiency.

The meta-cognitive module operates in two phases: a fast, approximate stage (MC1), and a deliberate, resource-intensive stage (MC2). MC1 quickly evaluates system 1's solution against available resources and the problem's reward potential. MC2, engaged when deeper analysis is warranted, assesses the cost-effectiveness of system 2 activation relative to anticipated reward improvements. This dual-phase approach aims to balance resource usage while maximizing decision accuracy; by mapping cognitive processes to AI architecture, this work advances meta-reasoning capabilities in computational systems.

Practical Implications and Instances

The paper outlines real-world applications of the SOFAI architecture in domains experiencing sequential decision problems. Two implementations are explored: constrained environment trajectory planning and epistemic planning tasks. The first tackles decision integrity in grid environments, while the latter leverages solvers capable of composing entire solution plans. These implementations demonstrate the architecture's flexibility across problem domains, with meta-cognitive agents optimizing solver engagement by leveraging domain knowledge and resource availability.

Conclusion

"Thinking Fast and Slow in AI: the Role of Metacognition" posits a compelling framework inspired by human cognition for enhancing AI systems. By integrating fast and slow decision processes with meta-cognitive oversight, the SOFAI architecture offers a promising pathway for AI that adapts flexibly to varied problem contexts. Future work may expand upon this foundation, potentially contributing to the development of more autonomous, generalizable AI systems capable of mirroring the nuanced decision-making inherent in human cognition.