Artificial Intelligence and Logic Programming

This book is an introduction to inductive logic programming (ILP), a research field at the intersection of machine learning and Logic Programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

This book tells the story of the development, during the 1960s and 1970s, of AI, the field that sought to get computers to do things that would be considered intelligent if a person did them.

Building the Second Mind: 1956 and the Origins of Artificial Intelligence Computing tells the history of the origins of AI. As the field that seeks to do things that would be considered intelligent if a human being did them, AI is a constant of human thought. Yet it was impossible until the digital computer was invented during WWII, and the field itself was not declared until 1956.

This book probes the past, present, and future of artificial intelligence, from its earliest philosophical and mathematical roots to tantalizing glimpses of 21st-century machines with superior intelligence and truly prodigious speed and memory.

This book is an overview of classical artificial intelligence (AI) programming via actual implementation of landmark systems (case studies). For the student interested in AI, Paradigms of Artificial Intelligence Programming is an invaluable history lesson. Even the programmer who is relatively uninterested in AI will find value in the book's basic introduction to Lisp and case studies written in Lisp.

In this accessible, comprehensive textbook, George Luger captures the essence of artificial intelligence solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, it presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Readers learn how to use a number of different software tools and techniques to address the many challenges faced by today's computer scientists.

This book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace.

This book is your essential guide to navigating this fascinating world, offering a deep dive into the core algorithms that power AI, and unraveling the mysteries that lie at the heart of this revolutionary technology.

Can computers be creative? Is algorithmic art just a form of Candy Crush? Cutting through the smoke and mirrors surrounding computation, robotics and artificial intelligence, Joanna Zylinska argues that, to understand the promise of AI for the creative fields, we must not confine ourselves solely to the realm of aesthetics. Instead, we need to address the role and position of the human in the current technical setup – including the associated issues of labour, robotisation and, last but not least, extinction.

Organisms, or organized, living, systems. What's their origin? Why and how did they evolve? This book analyses the evolution of complex structures in the universe - from the subatomic particles after the Big Bang onwards - in order to understand the emergence of today's interconnected society.

This book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two 'revolutions' in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age - prediction based on datasets.

This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work.

This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments.

Can humans and artificial intelligences share concepts and communicate? This book shows that philosophical work on the metaphysics of meaning can help answer these questions.

Covering the authors' own state-of-the-art research results, this book presents a rigorous, modern account of the mathematical methods and tools required for the semantic analysis of logic programs. It significantly extends the tools and methods from traditional order theory to include nonconventional methods from mathematical analysis that depend on topology, domain theory, generalized distance functions, and associated fixed-point theory. The authors closely examine the interrelationships between various semantics as well as the integration of logic programming and connectionist systems/neural networks.