Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

• URL: http://arxiv.org/abs/2406.12147v1
• Date: Mon, 17 Jun 2024 23:30:46 GMT
• Title: Metacognitive AI: Framework and the Case for a Neurosymbolic Approach
• Authors: Hua Wei, Paulo Shakarian, Christian Lebiere, Bruce Draper, Nikhil Krishnaswamy, Sergei Nirenburg,
• Abstract summary: 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.
• Score: 5.5441283041944
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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