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Author: Derek Partridge Publisher: Cambridge University Press ISBN: 9780521359443 Category : Computers Languages : en Pages : 516
Book Description
This outstanding collection is designed to address the fundamental issues and principles underlying the task of Artificial Intelligence.
Author: Derek Partridge Publisher: Cambridge University Press ISBN: 9780521359443 Category : Computers Languages : en Pages : 516
Book Description
This outstanding collection is designed to address the fundamental issues and principles underlying the task of Artificial Intelligence.
Author: Michael R. Genesereth Publisher: Morgan Kaufmann ISBN: 0128015543 Category : Computers Languages : en Pages : 406
Book Description
Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic. The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets. This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system. The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction. The third section introduces modal operators that facilitate representing and reasoning about knowledge. This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes. The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture. End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research. Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner. A bibliography and index complete this comprehensive work.
Author: Frank van Harmelen Publisher: Elsevier ISBN: 9780080557021 Category : Computers Languages : en Pages : 1034
Book Description
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily
Author: Mehryar Mohri Publisher: MIT Press ISBN: 0262351366 Category : Computers Languages : en Pages : 505
Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: Pei Wang Publisher: Springer Science & Business Media ISBN: 9491216627 Category : Computers Languages : en Pages : 334
Book Description
This book is a collection of writings by active researchers in the field of Artificial General Intelligence, on topics of central importance in the field. Each chapter focuses on one theoretical problem, proposes a novel solution, and is written in sufficiently non-technical language to be understandable by advanced undergraduates or scientists in allied fields. This book is the very first collection in the field of Artificial General Intelligence (AGI) focusing on theoretical, conceptual, and philosophical issues in the creation of thinking machines. All the authors are researchers actively developing AGI projects, thus distinguishing the book from much of the theoretical cognitive science and AI literature, which is generally quite divorced from practical AGI system building issues. And the discussions are presented in a way that makes the problems and proposed solutions understandable to a wide readership of non-specialists, providing a distinction from the journal and conference-proceedings literature. The book will benefit AGI researchers and students by giving them a solid orientation in the conceptual foundations of the field (which is not currently available anywhere); and it would benefit researchers in allied fields by giving them a high-level view of the current state of thinking in the AGI field. Furthermore, by addressing key topics in the field in a coherent way, the collection as a whole may play an important role in guiding future research in both theoretical and practical AGI, and in linking AGI research with work in allied disciplines
Author: Michael F. McTear Publisher: Springer Science & Business Media ISBN: 1447135423 Category : Computers Languages : en Pages : 393
Book Description
This book contains the edited versions of papers presented at the 3rd Irish Conference on Artificial Intelligence and Cognitive Science, which was held at the University of Ulster at Jordanstown, Northern Ireland on 20-21 September 1990. The main aims of this annual conference are to promote AI research in Ireland, to provide a forum for the exchange of ideas amongst the different disciplines concerned with the study of cognition, and to provide an opportunity for industry to see what research is being carried out in Ireland and how they might benefit from the results of this research. Although most of the partiCipants at the conference came from universities and companies within Ireland, a positive feature of the conference was the extent of interest shown outside of Ireland, resulting in partiCipants from USA, Canada, Austria, and England. The keynote speakers were Professor David Chin, University of Hawaii, and Professor Derek Partridge, University of Exeter, and the topics included machine learning, AI tools and methods, expert systems, speech, vision, natural language, reasoning with uncertain information, and explanation. The sponsors of the conference were Digital Equipment Co (Galway) and the Industrial Development Board for Northern Ireland.
Author: Mayuri Mehta Publisher: Springer Nature ISBN: 3031128079 Category : Technology & Engineering Languages : en Pages : 273
Book Description
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Author: Derek Partridge
Author: Derek Partridge
Author: Derek Partridge
Author: Michael R. Genesereth
Author: Frank van Harmelen
Author: Mehryar Mohri
Author: 
Author: Pei Wang
Author: Michael F. McTear
Author: Mayuri Mehta
Author: Ronald Chrisley