Metacognitive Artificial Intelligence

This thorough guide is essential for researchers, educators, and professionals interested in the self-assessment and optimization of AI systems. With contributions from experts across disciplines and many examples, it provides comprehensive insights into AI's decision-making processes and ensures safety and reliability in high-stakes applications.

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.