Knowledge Representation and Reasoning

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Author :
Publisher : Morgan Kaufmann
ISBN 13 : 1558609326
Total Pages : 414 pages
Book Rating : 4.27/5 ( download)

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Book Synopsis Knowledge Representation and Reasoning by : Ronald Brachman

Download or read book Knowledge Representation and Reasoning written by Ronald Brachman and published by Morgan Kaufmann. This book was released on 2004-05-19 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.

Knowledge Representation, Reasoning, and the Design of Intelligent Agents

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Author :
Publisher : Cambridge University Press
ISBN 13 : 1107782872
Total Pages : 363 pages
Book Rating : 4.77/5 ( download)

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Book Synopsis Knowledge Representation, Reasoning, and the Design of Intelligent Agents by : Michael Gelfond

Download or read book Knowledge Representation, Reasoning, and the Design of Intelligent Agents written by Michael Gelfond and published by Cambridge University Press. This book was released on 2014-03-10 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.

Knowledge Representation, Reasoning and Declarative Problem Solving

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Publisher : Cambridge University Press
ISBN 13 : 1139436449
Total Pages : 546 pages
Book Rating : 4.41/5 ( download)

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Book Synopsis Knowledge Representation, Reasoning and Declarative Problem Solving by : Chitta Baral

Download or read book Knowledge Representation, Reasoning and Declarative Problem Solving written by Chitta Baral and published by Cambridge University Press. This book was released on 2003-01-09 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: Baral shows how to write programs that behave intelligently, by giving them the ability to express knowledge and to reason. This book will appeal to practising and would-be knowledge engineers wishing to learn more about the subject in courses or through self-teaching.

Handbook of Knowledge Representation

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Author :
Publisher : Elsevier
ISBN 13 : 9780080557021
Total Pages : 1034 pages
Book Rating : 4.23/5 ( download)

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Book Synopsis Handbook of Knowledge Representation by : Frank van Harmelen

Download or read book Handbook of Knowledge Representation written by Frank van Harmelen and published by Elsevier. This book was released on 2008-01-08 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Foundations of Knowledge Representation and Reasoning

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Publisher : Springer Science & Business Media
ISBN 13 : 9783540581079
Total Pages : 372 pages
Book Rating : 4.73/5 ( download)

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Book Synopsis Foundations of Knowledge Representation and Reasoning by : Gerhard Lakemeyer

Download or read book Foundations of Knowledge Representation and Reasoning written by Gerhard Lakemeyer and published by Springer Science & Business Media. This book was released on 1994-06-28 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers collected in this book cover a wide range of topics in asymptotic statistics. In particular up-to-date-information is presented in detection of systematic changes, in series of observation, in robust regression analysis, in numerical empirical processes and in related areas of actuarial sciences and mathematical programming. The emphasis is on theoretical contributions with impact on statistical methods employed in the analysis of experiments and observations by biometricians, econometricians and engineers.

Principles of Knowledge Representation and Reasoning

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Author :
Publisher : Morgan Kaufmann
ISBN 13 :
Total Pages : 680 pages
Book Rating : 4.81/5 ( download)

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Book Synopsis Principles of Knowledge Representation and Reasoning by : Jon Doyle

Download or read book Principles of Knowledge Representation and Reasoning written by Jon Doyle and published by Morgan Kaufmann. This book was released on 1994 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings of KR '94 comprise 55 papers on topics including deduction an search, description logics, theories of knowledge and belief, nonmonotonic reasoning and belief revision, action and time, planning and decision-making and reasoning about the physical world, and the relations between KR

Knowledge Representation and Reasoning

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Author :
Publisher : Elsevier
ISBN 13 : 008048932X
Total Pages : 381 pages
Book Rating : 4.22/5 ( download)

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Book Synopsis Knowledge Representation and Reasoning by : Ronald Brachman

Download or read book Knowledge Representation and Reasoning written by Ronald Brachman and published by Elsevier. This book was released on 2004-06-17 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. Authors are well-recognized experts in the field who have applied the techniques to real-world problems Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems Offers the first true synthesis of the field in over a decade

Representing and Reasoning with Probabilistic Knowledge

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Publisher : Cambridge, Mass. : MIT Press
ISBN 13 :
Total Pages : 264 pages
Book Rating : 4.40/5 ( download)

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Book Synopsis Representing and Reasoning with Probabilistic Knowledge by : Fahiem Bacchus

Download or read book Representing and Reasoning with Probabilistic Knowledge written by Fahiem Bacchus and published by Cambridge, Mass. : MIT Press. This book was released on 1990 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.

Graph-based Knowledge Representation

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Publisher : Springer Science & Business Media
ISBN 13 : 1848002866
Total Pages : 428 pages
Book Rating : 4.69/5 ( download)

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Book Synopsis Graph-based Knowledge Representation by : Michel Chein

Download or read book Graph-based Knowledge Representation written by Michel Chein and published by Springer Science & Business Media. This book was released on 2008-10-20 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a de?nition and study of a knowledge representation and r- soning formalism stemming from conceptual graphs, while focusing on the com- tational properties of this formalism. Knowledge can be symbolically represented in many ways. The knowledge representation and reasoning formalism presented here is a graph formalism – knowledge is represented by labeled graphs, in the graph theory sense, and r- soning mechanisms are based on graph operations, with graph homomorphism at the core. This formalism can thus be considered as related to semantic networks. Since their conception, semantic networks have faded out several times, but have always returned to the limelight. They faded mainly due to a lack of formal semantics and the limited reasoning tools proposed. They have, however, always rebounded - cause labeled graphs, schemas and drawings provide an intuitive and easily und- standable support to represent knowledge. This formalism has the visual qualities of any graphic model, and it is logically founded. This is a key feature because logics has been the foundation for knowledge representation and reasoning for millennia. The authors also focus substantially on computational facets of the presented formalism as they are interested in knowledge representation and reasoning formalisms upon which knowledge-based systems can be built to solve real problems. Since object structures are graphs, naturally graph homomorphism is the key underlying notion and, from a computational viewpoint, this moors calculus to combinatorics and to computer science domains in which the algorithmicqualitiesofgraphshavelongbeenstudied,asindatabasesandconstraint networks.

Graph Structures for Knowledge Representation and Reasoning

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Author :
Publisher : Springer Nature
ISBN 13 : 3030723089
Total Pages : 158 pages
Book Rating : 4.88/5 ( download)

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Book Synopsis Graph Structures for Knowledge Representation and Reasoning by : Michael Cochez

Download or read book Graph Structures for Knowledge Representation and Reasoning written by Michael Cochez and published by Springer Nature. This book was released on 2021-04-16 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities.