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.

‘This book offers a fascinating exploration of the astounding relationship between metacognition and AI. It provides readers with a comprehensive understanding of how AI systems can be designed not only to make accurate predictions but also to learn from their mistakes and improve over time. The authors explore various methods for enhancing trust in AI models by incorporating aspects of human cognitive processes, providing practical insights for building more reliable and transparent AI technologies.’

Todd C. Hughes - Scientific Systems Chief Innovation Officer

‘This book on metacognitive AI addresses a timely and critical question in the general field of artificial intelligence: how to make AI systems more reliable and self-aware. The book strikes a good balance between theories, methods, and applications. It is an invaluable resource for researchers and practitioners.’

Hanghang Tong - University of Illinois Urbana-Champaign