Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- 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
- 15 Robust and Compositional Concept Grounding for Image Generative AI
- 16 mLINK: Machine Learning Integration with Network and Knowledge
16 - mLINK: Machine Learning Integration with Network and Knowledge
from Part VIII - Applications of Metacognitive AI
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- 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
- 15 Robust and Compositional Concept Grounding for Image Generative AI
- 16 mLINK: Machine Learning Integration with Network and Knowledge
Summary
We investigate the incorporation of metacognitive capabilities into Machine Learning Integrated with Network (MLIN) systems and develop machine Learning Integrated with Knowledge (mLINK) strata. This stratum is aimed at integrating knowledge obtained from multiple MLIN elements and reflecting on the ML application performance outcomes in order to provide feedback on metacognitive actions aimed at ensuring performance and improving ML application robustness towards Data Quality (DQ) variations. We discuss multiple use cases to show how the knowledge on the interrelationships between MLIN components, DQ, and ML application performance can be generated and employed by mLINK. We elaborate on how this knowledge is integrated into mLINK to produce metaknowledge, deemed as recommendations on adaptation actions or strategies needed. We define the process of employing these recommendations by mLINK as metacognition and describe multiple examples of utilizing these metacognitive strategies in practice, such as optimizing the data collection; reflection on DQ; DQ assurance; enhanced transfer learning; and Federated Learning for enhancing security, privacy, collaboration, and communication in MLIN.
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- Metacognitive Artificial Intelligence , pp. 264 - 275Publisher: Cambridge University PressPrint publication year: 2025
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