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A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications. This textbook offers a comprehensive survey of artificial intelligence (AI) algorithms and programming organization for robot systems. Readers who master the topics covered will be able to design and evaluate an artificially intelligent robot for applications involving sensing, acting, planning, and learning. A background in AI is not required; the book introduces key AI topics from all AI subdisciplines throughout the book and explains how they contribute to autonomous capabilities. This second edition is a major expansion and reorganization of the first edition, reflecting the dramatic advances made in AI over the past fifteen years. An introductory overview provides a framework for thinking about AI for robotics, distinguishing between the fundamentally different design paradigms of automation and autonomy. The book then discusses the reactive functionality of sensing and acting in AI robotics; introduces the deliberative functions most often associated with intelligence and the capability of autonomous initiative; surveys multi-robot systems and (in a new chapter) human-robot interaction; and offers a “metaview” of how to design and evaluate autonomous systems and the ethical considerations in doing so. New material covers locomotion, simultaneous localization and mapping, human-robot interaction, machine learning, and ethics. Each chapter includes exercises, and many chapters provide case studies. Endnotes point to additional reading, highlight advanced topics, and offer robot trivia.
Offering an introduction to the field of expert/knowledge based systems, this text covers current and emerging trends as well as future research areas. It considers both the system shell and programming environment approaches to expert system development.;College or university bookshops may order five or more copies at a special student price. Price is available on request.
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.
Reasoning under uncertainty is always based on a specified language or for malism, including its particular syntax and semantics, but also on its associated inference mechanism. In the present volume of the handbook the last aspect, the algorithmic aspects of uncertainty calculi are presented. Theory has suffi ciently advanced to unfold some generally applicable fundamental structures and methods. On the other hand, particular features of specific formalisms and ap proaches to uncertainty of course still influence strongly the computational meth ods to be used. Both general as well as specific methods are included in this volume. Broadly speaking, symbolic or logical approaches to uncertainty and nu merical approaches are often distinguished. Although this distinction is somewhat misleading, it is used as a means to structure the present volume. This is even to some degree reflected in the two first chapters, which treat fundamental, general methods of computation in systems designed to represent uncertainty. It has been noted early by Shenoy and Shafer, that computations in different domains have an underlying common structure. Essentially pieces of knowledge or information are to be combined together and then focused on some particular question or domain. This can be captured in an algebraic structure called valuation algebra which is described in the first chapter. Here the basic operations of combination and focus ing (marginalization) of knowledge and information is modeled abstractly subject to simple axioms.
Foundations of Deductive Databases and Logic Programming focuses on the foundational issues concerning deductive databases and logic programming. The selection first elaborates on negation in logic programming and towards a theory of declarative knowledge. Discussions focus on model theory of stratified programs, fixed point theory of nonmonotonic operators, stratified programs, semantics for negation in terms of special classes of models, relation between closed world assumption and the completed database, negation as a failure, and closed world assumption. The book then takes a look at negation as failure using tight derivations for general logic programs, declarative semantics of logic programs with negation, and declarative semantics of deductive databases and logic programs. The publication tackles converting AND-control to OR-control by program transformation, optimizing dialog, equivalences of logic programs, unification, and logic programming and parallel complexity. Topics include parallelism and structured and unstructured data, parallel algorithms and complexity, solving equations, most general unifiers, systems of equations and inequations, equivalences of logic programs, and optimizing recursive programs. The selection is a valuable source of data for researchers interested in pursuing further studies on the foundations of deductive databases and logic programming.
The Handbook of Logic in Artificial Intelligence and Logic Programming is a multi-volume work covering all major areas of the application of logic to artificial intelligence and logic programming. The authors are chosen on an international basis and are leaders in the fields covered. Volume 5 is the last in this well-regarded series. Logic is now widely recognized as one of the foundational disciplines of computing. It has found applications in virtually all aspects of the subject, from software and hardware engineering to programming languages and artificial intelligence. In response to the growing need for an in-depth survey of these applications the Handbook of Logic in Artificial Intelligence and its companion, the Handbook of Logic in Computer Science have been created. The Handbooks are a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying themes in the various areas. Some mathematical background is assumed, and much of the material will be of interest to logicians and mathematicians. Volume 5 focuses particularly on logic programming. The chapters, which in many cases are of monograph length and scope, emphasize possible unifying themes.
Logic and databases are inextricably intertwined. The relational model in particular is essentially just elementary predicate logic, tailored to fit the needs of database management. Now, if you're a database professional, I'm sure this isn't news to you; but you still might not realize just how much everything we do in the database world is - or should be! - affected by predicate logic. Logic is everywhere. So if you're a database professional you really owe it to yourself to understand the basics of formal logic, and you really ought to be able to explain (and perhaps defend) the connections between formal logic and database management. And that's what this book is about. What it does is show, through a series of partly independent and partly interrelate essays, just how various crucial aspects of database technology-some of them very familiar, others maybe less so- are solidly grounded in formal logic. It is divided into five parts: *Basic Logic *Logic and Database Management *Logic and Database Design *Logic and Algebra *Logic and the Third Manifesto There's also a lengthy appendix, containing a collection of frequently asked questions (and some answers) on various aspects of logic and database management. Overall, my goal is to help you realize the importance of logic in everything you do, and also- I hope- to help you see that logic can be fun.
This book constitutes the refereed proceedings of the 19th International Conference on Database and Expert Systems Applications, DEXA 2008, held in Turin, Italy, in September 2008. The 74 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 208 submissions. The papers are organized in topical sections on data privacy; temporal, spatial and high dimensional databases; semantic Web and ontologies; query processing; Web and information retrieval; mobile data and information; data and information streams; data mining algorithms; multimedia databases; data mining systems, data warehousing, OLAP; data and information semantics; XML databases; applications of database, information, and decision support systems; and schema, process and knowledge modelling and evolution.
The capability to reason in a world full of uncertainties, vagueness and ignorance is what distinguishes humans. This ability to argument in a partially known world is the informal definition of common-sense reasoning. The question how common-sense reasoning is performed occupied humanity since we can think of. Last century this issue reached an immense importance. Especially during the last three decades the study of common-sense reasoning became one of the major research topics in Artificial Intelligence (AI). Several formalisms to capture the mechanism of common-sense reasoning have been proposed so far. This book concentrates on presenting the most important formalisms for common-sense reasoning, and, showing that one of the discussed formalisms serves perfectly to capture the mechanism of common-sense reasoning, since this formalism subsumes all other in this book introduced formalisms dealing with common-sense reasoning.