A Robotic Framework For The Mobile Manipulator

By proposing and forming a mobile manipulator for modern multi-floor buildings, A Robotic Framework for the Mobile Manipulator: Theory and Application helps readers visualize an end-to-end workflow for making a robot system work in a targeted environment. From a product-oriented viewpoint, this book is considered as a bridge from theories to real products, in which robotic software modules and the robotic system integration are mainly concerned. In the end, readers will have an overview of how to build and integrate various single robotic modules to execute a list of designed tasks in the real world, as well as how to make a robot system work independently, without human interventions. With references and execution guidelines provided at the end of each chapter, the book will be a useful tool for developers and researchers looking to expand their knowledge about the robotics and the robotic software.

"This book helps readers visualize an end-to-end workflow for making a robot system work in a targeted environment. It is considered as a bridge from theories to real products, in which robotic software modules and the robotic system integration are mainly concerned"--

Mobile manipulators combine the advantages of mobile platforms and robotic arms, extending their operational range and functionality to large spaces and remote, demanding, and/or dangerous environments. They also bring complexity and difficulty in dynamic modeling and control system design. However, advances in nonlinear system analysis and control

This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: • Contributes to the topic of explainable artificial intelligence (XAI) • Focuses on the XAI subtopic of explainable agency • Includes an introductory chapter, a survey, and five other original contributions

An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior. The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences? This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background.

AI in and for Africa: A Humanistic Perspective explores the convoluted intersection of artificial intelligence (AI) with Africa’s unique socio-economic realities. This book is the first of its kind to provide a comprehensive overview of how AI is currently being deployed on the African continent. Given the existence of significant disparities in Africa related to gender, race, labour, and power, the book argues that the continent requires different AI solutions to its problems, ones that are not founded on technological determinism or exclusively on the adoption of Eurocentric or Western-centric worldviews. It embraces a decolonial approach to exploring and addressing issues such as AI’s diversity crisis, the absence of ethical policies around AI that are tailor-made for Africa, the ever-widening digital divide, and the ongoing practice of dismissing African knowledge systems in the contexts of AI research and education. Although the book suggests a number of humanistic strategies with the goal of ensuring that Africa does not appropriate AI in a manner that is skewed in favour of a privileged few, it does not support the notion that the continent should simply opt for a "one-size-fits-all" solution either. Rather, in light of Africa’s rich diversity, the book embraces the need for plurality within different regions’ AI ecosystems. The book advocates that Africa-inclusive AI policies incorporate a relational ethics of care which explicitly addresses how Africa’s unique landscape is entwined in an AI ecosystem. The book also works to provide actionable AI tenets that can be incorporated into policy documents that suit Africa’s needs. This book will be of great interest to researchers, students, and readers who wish to critically appraise the different facets of AI in the context of Africa, across many areas that run the gamut from education, gender studies, and linguistics to agriculture, data science, and economics. This book is of special appeal to scholars in disciplines including anthropology, computer science, philosophy, and sociology, to name a few.

As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.

Imagine a world where the boundaries of creativity are not only stretched but redefined. This book serves as your guide to this new frontier, engaging general readers, tech enthusiasts, and creatives alike in the captivating interplay between human ingenuity and artificial intelligence (AI). Journey through the ground-breaking advancements in AI as they intersect with art, design, entertainment, and education. Discover how AI’s power to analyze and understand language can be harnessed to generate breathtaking visuals from mere text descriptions—a process known as text-conditional image generation. But this book goes beyond just showcasing AI’s capabilities: it delves into its transformative effects on the creative process itself. How will artists and designers adapt to a world where they co-create with machines? What are the implications of AI-generated art in educational settings? This book tackles these questions head on, offering a comprehensive view of the changing landscape of creativity. At its core, this book challenges you to rethink what’s possible in the realm of artistic expression. Manu contends that as AI evolves, mastering the art of collaboration between human and machine will become essential. More than just a look into the future, Transcending Imagination: Artificial Intelligence and the Future of Creativity is a roadmap for artists, designers, and educators eager to navigate the uncharted territory of AI-augmented creativity. It is a must-read for anyone interested in how AI might redefine the realms of art, design, and education.