"This book covers the latest and cutting-edge application technology of Adaptive AI in various fields. It can provide relevant practitioners with ideas to solve problems and have a deeper understanding of Adaptive AI. At the same time, guide and help Adaptive AI and related industries to deepen their understanding of the industry and enhance their professional knowledge and skills"--

The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence , edited by Zhihan Lv of Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation issues in AI systems. It explores the latest concepts, technologies, and applications of adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, the handbook presents real-world cases, along with thorough analysis and relatable examples, making it an invaluable resource for practitioners seeking practical ideas and solutions in the field. Additionally, the book serves as a teaching material and reference guide for students and educators in AI-related disciplines, ensuring a deep understanding and exploration of the emerging discipline of Adaptive AI. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems. In a rapidly changing landscape, Principles and Applications of Adaptive Artificial Intelligence becomes the ultimate guide for researchers, professionals, and students, enabling them to unleash the full potential of Adaptive AI and revolutionize their research and applications.

The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence, edited by Zhihan Lv from Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation problem in AI systems. It explores the latest concepts, technologies, and applications of Adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, real-world case studies, and thorough analysis, the Handbook provides practitioners with practical ideas and solutions, while also serving as a valuable teaching material and reference guide for students and educators in AI-related disciplines. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems, making it an indispensable resource for researchers, professionals, and students seeking to revolutionize their research and applications in the field of Adaptive AI.

Teaches students about classical and nonclassical adaptive systems within one pair of covers Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems

“Artificial Intelligence and Machine Learning – Principles and Applications” is a comprehensive guide that delves into the core concepts, methodologies, and practical implementations of AI and machine learning. Authored with clarity and expertise, it serves as an indispensable resource for both beginners and seasoned professionals in the field. The book begins by elucidating the fundamental principles underlying artificial intelligence and machine learning, providing readers with a solid foundation to build upon. From there, it progresses into more advanced topics, covering a wide range of algorithms, techniques, and applications across various domains. Readers are guided through the intricacies of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning. Each concept is accompanied by illustrative examples and offers a hands-on approach to learning. Furthermore, the book explores the ethical and societal implications of AI and machine learning, prompting readers to consider the broader implications of their work. It discusses issues such as bias, fairness, privacy, and transparency, encouraging a responsible approach to AI development and deployment. One of the standout features of “Artificial Intelligence and Machine Learning – Principles and Applications” is its emphasis on practical applications. It provides insights into how AI and machine learning techniques can be leveraged to solve complex problems in areas such as healthcare, finance, marketing, and beyond. Overall, this book serves as an invaluable resource for anyone looking to gain a comprehensive understanding of artificial intelligence and machine learning, offering both theoretical insights and practical guidance for real-world implementation.

These original contributions provide a unique opportunity for researchers and computing professionals, engineers, and managers to explore both the principles underlying basic AI research and their application in practice. The first part of the book describes work in five areas of AI research that is currently at the stage where it can be implemented in practical programs. These areas include blackboard architectures and systems, learning algorithms and strategies, neural networks, adaptive learning using pattern recognition, and signal processing. The second part describes six systems, designed for a wide variety of applications, that are now either in operation or at an advanced stage of development; intelligent techniques for spectral estimation, expert systems applied to antenatal assessment of fetal well-being, AI in the processing of underwater acoustic data, automatic speech recognition using neural networks, fault diagnosis of microwave digital radio, and waveguide filter alignment using adaptive learning techniques. A. R. Mirzai is a Research Fellow in the Department of Electrical Engineering at the University of Edinburgh. Artificial Intelligence: Concepts and Applications is included in the Artificial Intelligence series, edited by Michael Brady, Daniel Bobrow, and Randall Davis.

Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Artificial Intelligence (AI) and the Internet of Things (IoT) are growing rapidly in today’s business world. In today's era, 25 billion devices, including machines, sensors, and cameras, are connected and continue to grow steadily. It is assumed that in 2025, 41.6 billion IoT devices will be connected, generating around 79.4 zettabytes of data. IoT and AI are intersecting in various scenarios. IoT-enabled devices are generating a huge amount of data, and with the help of AI, this data is used to build various intelligent models. These intelligent models are helpful in our daily lives and make the world smarter. Artificial Intelligence in Cyber Physical Systems: Principles and Applications addresses issues related to system safety, security, reliability, and deployment strategies in healthcare, military, transportation, energy, infrastructure, smart homes, and smart cities.