Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

TL;DR

This paper proposes a framework for metacognitive AI called TRAP, emphasizing transparency, reasoning, adaptation, and perception, and advocates for a neurosymbolic approach to enhance AI's self-awareness and reasoning capabilities.

Contribution

It introduces the TRAP framework for metacognitive AI and argues for leveraging neurosymbolic AI to address metacognitive challenges.

Findings

TRAP framework clarifies key aspects of metacognitive AI

Neurosymbolic AI can effectively support metacognitive functions

Position paper advocating for neurosymbolic approaches in metacognition

Abstract

Metacognition is the concept of reasoning about an agent's own internal processes and was originally introduced in the field of developmental psychology. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.