Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- 2 An Architectural Approach to Metacognition
- 3 Metacognitive AI through Error Detection and Correction Rules
- 4 Mutual Trust in Human–AI Teams Relies on Metacognition
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
3 - Metacognitive AI through Error Detection and Correction Rules
from Part II - Taxonomy of Metacognitive Approaches
Published online by Cambridge University Press: 08 September 2025
Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- 2 An Architectural Approach to Metacognition
- 3 Metacognitive AI through Error Detection and Correction Rules
- 4 Mutual Trust in Human–AI Teams Relies on Metacognition
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
Summary
Classification of movement trajectories has many applications in transportation. Supervised neural models represent the current state-of-the-art. Recent security applications require this task to be rapidly employed in environments that may differ from the data used to train such models for which there is little training data. We provide a neuro-symbolic rule-based framework to conduct error correction and detection of these models to support eventual deployment in security applications.
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- Metacognitive Artificial Intelligence , pp. 44 - 55Publisher: Cambridge University PressPrint publication year: 2025
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