Thursday, 4 December 2014

14 Ebooks For Learning AI And Robotics

In 2013, Google had acquired several new robotics firms. This acquisition showed that the company was seriously considering the field for perhaps its next big innovation. Well, whether that happens on not, these have always been big fields in the world of tech. Here are 15 ebooks that will give you a clearer picture. Did we mention, most of these books are available for free!

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1. Petri Net, Theory and Applications

The intuitively appealing graphical notation makes Petri nets the model of choice in many applications. The natural way in which Petri nets allow one to formally capture many of the basic notions and issues of concurrent systems has contributed greatly to the development of a rich theory of concurrent systems based on Petri nets. This book brings together reputable researchers from all over the world in order to provide a comprehensive coverage of advanced and modern topics not yet reflected by other books. The book consists of 23 chapters written by 53 authors from 12 different countries.

2. Reinforcement Learning

Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.

3. Medical Robotics

The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not.

4. Machine Learning, Neural and Statistical Classification

This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems.

5. Multiprocessor Scheduling, Theory and Applications

A major goal of the book is to continue a good tradition - to bring together reputable researchers from different countries in order to provide a comprehensive coverage of advanced and modern topics in scheduling not yet reflected by other books. The virtual consortium of the authors has been created by using electronic exchanges; it comprises 50 authors from 18 different countries who have submitted 23 contributions to this collective product. In this sense, the volume can be added to a bookshelf with similar collective publications in scheduling, started by Coffman (1976) and successfully continued by Chretienne et al. (1995), Gutin and Punnen (2002), and Leung (2004). This volume contains four major parts that cover the following directions: the state of the art in theory and algorithms for classical and non-standard scheduling problems; new exact optimization algorithms, approximation algorithms with performance guarantees, heuristics and metaheuristics; novel models and approaches to scheduling; and, last but least, several real-life applications and case studies.

6. Human Robot Interaction

HRI is an extremely active research field where new and important work is being published at a fast pace. It is neither possible nor is it our intention to cover every important work in this important research field in one volume. However, we believe that HRI as a research field has matured enough to merit a compilation of the outstanding work in the field in the form of a book. This book, which presents outstanding work from the leading HRI researchers covering a wide spectrum of topics, is an effort to capture and present some of the important contributions in HRI in one volume. We hope that this book will benefit both experts and novice and provide a thorough understanding of the exciting field of HRI.

7. Planning Algorithms

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

8. Rehabilitation Robotics

The coupling of several areas of the medical field with recent advances in robotic systems has seen a paradigm shift in our approach to selected sectors of medical care, especially over the last decade. Rehabilitation medicine is one such area. The development of advanced robotic systems has ushered with it an exponential number of trials and experiments aimed at optimising restoration of quality of life to those who are physically debilitated. Despite these developments, there remains a paucity in the presentation of these advances in the form of a comprehensive tool. This book was written to present the most recent advances in rehabilitation robotics known to date from the perspective of some of the leading experts in the field and presents an interesting array of developments put into 33 comprehensive chapters. The chapters are presented in a way that the reader will get a seamless impression of the current concepts of optimal modes of both experimental and ap- plicable roles of robotic devices.

9. Parallel Manipulators, Towards New Applications

In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications.

10. The LION Way: Machine Learning plus Intelligent Optimisation

Topics included: Introduction and nearest neighbors • Learning requires a method • Linear models • Mastering generalized linear least-squares • Rules, decision trees, and forests • Ranking and selecting features • Specific nonlinear models • Neural networks, shallow and deep • Statistical Learning Theory and Support Vector Machines (SVM) • Democracy in Machine Learning.

11. A Course in Machine Learning

Topics covered include: decision trees, geometry and nearest neighbors, the perceptron, machine learning in practice, beyond binary classification, linear models, probabilistic modeling, neural networks, kernel methods, learning theory, ensemble methods, efficient learning,,unsupervised learning,,expectation maximization, semi-supervised learning, graphical models, online learning, structured learning and bayesian learning.

12. AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java

This book is designed for three primary purposes. The first is as a programming language component of a general class in Artificial Intelligence. From this viewpoint, the authors see as essential that the AI student build the significant algorithms that support the practice of AI. This book is designed to present exactly these algorithms. However, in the normal lecture/lab approach taken to teaching Artificial Intelligence at the University level, we have often found that it is difficult to cover more than one language per quarter or semester course. Therefore we expect that the various parts of this material, those dedicated to either Lisp, Prolog, or Java, would be used individually to support programming the data structures and algorithms presented in the AI course itself. In a more advanced course in AI it would be expected that the class cover more than one of these programming paradigms.

13. Computer Vision: Models, Learning, and Inference

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision.

14. The Quest for Artificial Intelligence: A History of Ideas and Achievements

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. AI is becoming more and more a part of everyone’s life.

The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book’s many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.
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