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Sept. 16, 2025, 6:28 a.m.
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(Last updated: Sept. 16, 2025, 6:33 a.m.)
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| Shakarian P., Wei H. Metacognitive Artificial Intelligence 2025.pdf | 14.5 MB |
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SOURCE: Shakarian P., Wei H. Metacognitive Artificial Intelligence 2025
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COVER
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MEDIAINFO
Textbook in PDF format This groundbreaking volume is designed to meet the burgeoning needs of the research community and industry. This book delves into the critical aspects of AI's self-assessment and decision-making processes, addressing the imperative for safe and reliable AI systems in high-stakes domains such as autonomous driving, aerospace, manufacturing, and military applications. Featuring contributions from leading experts, the book provides comprehensive insights into the integration of metacognition within AI architectures, bridging symbolic reasoning with neural networks, and evaluating learning agents' competency. Key chapters explore assured Machine Learning, handling AI failures through metacognitive strategies, and practical applications across various sectors. Covering theoretical foundations and numerous practical examples, this volume serves as an invaluable resource for researchers, educators, and industry professionals interested in fostering transparency and enhancing reliability of AI systems. Preface Part I Introduction Metacognitive AI Part II Taxonomy of Metacognitive Approaches An Architectural Approach to Metacognition Metacognitive AI through Error Detection and Correction Rules Mutual Trust in Human–AI Teams Relies on Metacognition Part III Neuro-Symbolic Models in AI Learning Where and When to Reason in Neurosymbolic Inference Assessment of Competency of Learning Agents via Inference of Temporal Logic Formulas Part IV Metacognition with LLMS Metacognitive Intervention for Accountable LLMs through Sparsity Metacognitive Insights into ChatGPT’s Arithmetic Reasoning Part V Metacognition in Learning Agents Uncertainty Quantifcation’s Role in Metacognition The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning Part VI Assured Machine Learning in High-Stakes Domains Toward Certifably Trustworthy Deep Learning at Scale Metacognition with Neural Network Verifcation and Repair Using Veritex Part VII Metacognition as a Solution to Handle Failure Reasoning about Anomalous Object Interaction Using Plan Failure as a Metacognitive Tractable Probabilistic Reasoning for Trustworthy AI Part VIII Applications of Metacognitive AI Robust and Compositional Concept Grounding for Image Generative AI mLINK: Machine Learning Integration with Network and Knowledge Military Applications of Artifcial Intelligence Metacognition
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