Artificial Intelligence A New Synthesis
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Artificial Intelligence
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Author : Nils J. Nilsson
language : en
Publisher: Morgan Kaufmann
Release Date : 1998
Artificial Intelligence written by Nils J. Nilsson and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
This new book, by one of the most respected researchers in Artificial Intelligence, features a radical new 'evolutionary' organization that begins with low level intelligent behavior and develops complex intelligence as the book progresses.
The Quest For Artificial Intelligence
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Author : Nils J. Nilsson
language : en
Publisher: Cambridge University Press
Release Date : 2009-10-30
The Quest For Artificial Intelligence written by Nils J. Nilsson and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-30 with Computers categories.
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.
Lifelong Machine Learning
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Author : Zhiyuan Chen
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-08-14
Lifelong Machine Learning written by Zhiyuan Chen and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-14 with Computers categories.
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
Planning With Markov Decision Processes
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Author : Mausam
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2012
Planning With Markov Decision Processes written by Mausam and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
Provides a concise introduction to the use of Markov Decision Processes for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms.
Deterministic Artificial Intelligence
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Author : Timothy Sands
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-05-27
Deterministic Artificial Intelligence written by Timothy Sands and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-27 with Computers categories.
Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.
Adversarial Machine Learning
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Author : Yevgeniy Vorobeychik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-08-08
Adversarial Machine Learning written by Yevgeniy Vorobeychik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-08 with Computers categories.
This is a technical overview of the field of adversarial machine learning which has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicious objects they develop. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
Artificial Intelligence From Beginning To Date
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Author : Zixing Cai
language : en
Publisher: World Scientific
Release Date : 2021-05-25
Artificial Intelligence From Beginning To Date written by Zixing Cai and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-25 with Computers categories.
This English edition monograph is developed and updated from China's best-selling, and award-winning, book on Artificial Intelligence (AI). It covers the foundations as well as the latest developments of AI in a comprehensive and systematic manner. It is a valuable guide for students and researchers on artificial intelligence.A wide range of topics in AI are covered in this book with four distinct features. First of all, the book comprises a comprehensive system, covering the core technology of AI, including the basic theories and techniques of 'traditional' artificial intelligence, and the basic principles and methods of computational intelligence. Secondly, the book focuses on innovation, covering advanced learning methods for machine learning and deep learning techniques and other artificial intelligence that have been widely used in recent years. Thirdly, the theory and practice of the book are highly integrated. There are theories, techniques and methods, as well as many application examples, which will help readers to understand the artificial intelligence theory and its application development. Fourthly, the content structure of the book is quite characteristic, consisting of three parts: (i) knowledge-based artificial intelligence, (ii) data-based artificial intelligence, and (iii) artificial intelligence applications.It is closely related to the core elements of artificial intelligence, namely knowledge, data, algorithms, and computing powers. This reflects the authors' deep understanding of the artificial intelligence discipline.
Artificial Intelligence
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Author : R. B. Mishra
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2010-10
Artificial Intelligence written by R. B. Mishra and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10 with Computers categories.
This book has been written keeping in view the requirements of undergraduate and postgraduate students and research scholars in the area of computer science and engineering in particular, and other branches of engineering which deal with the study of AI such as electronics engineering, electrical engineering, industrial engineering (robotics and FMS). Besides the engineering students, the postgraduate students of computer science and computer applications and cognitive sciences researchers can equally benefit from this text. The basic concepts of artificial intelligence, together with knowledge representation, reasoning methods, acquisition, management and distributed architecture, have been nicely and instructively described. The various application domains and disciplines in engineering, management, medicine which cover different aspects of design, assembly and monitoring, have been presented with utility aspects of AI concepts in logic and knowledge. The book maintains a simple and comprehensible style of presentation for the different categories of readers such as students, researchers and professionals for their respective uses.
Robot Learning From Human Teachers
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Author : Sonia Chernova
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2014-04-01
Robot Learning From Human Teachers written by Sonia Chernova and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-01 with Computers categories.
Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.