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Books in Reasoning

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    • Uncertainty in Artificial Intelligence 5

      • 1st Edition
      • Volume 10
      • March 20, 2017
      • R.D. Shachter + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 4 8 8 7 3 9 9
      • eBook
        9 7 8 1 4 8 3 2 9 6 5 5 5
      This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding.

    • Case-Based Reasoning

      • 1st Edition
      • June 28, 2014
      • Janet Kolodner
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 3 7 3
      • eBook
        9 7 8 1 4 8 3 2 9 4 4 9 0
      Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made.This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

    • Knowledge-Based Configuration

      • 1st Edition
      • March 31, 2014
      • Alexander Felfernig + 3 more
      • English
      • Hardback
        9 7 8 0 1 2 4 1 5 8 1 7 7
      • eBook
        9 7 8 0 1 2 4 1 5 8 6 9 6
      Knowledge-based Configuration incorporates knowledge representation formalisms to capture complex product models and reasoning methods to provide intelligent interactive behavior with the user. This book represents the first time that corporate and academic worlds collaborate integrating research and commercial benefits of knowledge-based configuration. Foundational interdisciplinary material is provided for composing models from increasingly complex products and services. Case studies, the latest research, and graphical knowledge representations that increase understanding of knowledge-based configuration provide a toolkit to continue to push the boundaries of what configurators can do and how they enable companies and customers to thrive.

    • Principles of Knowledge Representation and Reasoning

      • 1st Edition
      • June 5, 2014
      • Jon Doyle + 2 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 2 8 8
      • eBook
        9 7 8 1 4 8 3 2 1 4 5 2 8
      Principles of Knowledge Representation and Reasoning contains the proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning (KR '94) held in Bonn, Germany, on May 24-27, 1994. The conference provided a forum for reviewing the theory and principles underlying knowledge representation and reasoning. Topics covered range from reasoning about mental states and spatial reasoning with propositional logics to default logic as a query language. Comprised of 60 chapters, this book begins with a description of a formal language for representing and reasoning about time and action before turning to proof in context and how it can replace the most common uses of reflection principles. The reader is then introduced to reasoning with minimal models; belief ascription and mental-level modeling; and a unified framework for class-based representation formalisms. A general approach to specificity in default reasoning is also described, together with an ontology for engineering mathematics and the use of abduction to generate tests. The book concludes by considering the use of natural language for knowledge representation and reasoning. This monograph will be of interest to both students and practitioners in the fields of artificial intelligence and computer science.

    • Handbook of Temporal Reasoning in Artificial Intelligence

      • 1st Edition
      • Volume 1
      • March 1, 2005
      • Michael David Fisher + 2 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 1 4 9 3 6
      • Paperback
        9 7 8 0 4 4 4 5 5 8 8 1 7
      • eBook
        9 7 8 0 0 8 0 5 3 3 3 6 0
      This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence. Yet, until now, there has been no single volume collecting together the breadth of work in this area. This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the filed of Artificial Intelligence.Key Features:- Broad range: foundations; techniques and applications- Leading researchers around the world have written the chapters- Covers many vital applications- Source book for Artificial Intelligence, temporal reasoning- Approaches provide foundation for many future software systems

    • Knowledge Representation and Reasoning

      • 1st Edition
      • May 19, 2004
      • Ronald Brachman + 1 more
      • English
      • Hardback
        9 7 8 1 5 5 8 6 0 9 3 2 7
      • Paperback
        9 7 8 1 4 9 3 3 0 3 7 9 3
      • eBook
        9 7 8 0 0 8 0 4 8 9 3 2 2
      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.

    • Many-Dimensional Modal Logics: Theory and Applications

      • 1st Edition
      • Volume 148
      • October 21, 2003
      • A. Kurucz + 3 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 0 8 2 6 3
      • Paperback
        9 7 8 0 4 4 4 5 5 1 8 3 2
      • eBook
        9 7 8 0 0 8 0 5 3 5 7 8 4
      Modal logics, originally conceived in philosophy, have recently found many applications in computer science, artificial intelligence, the foundations of mathematics, linguistics and other disciplines. Celebrated for their good computational behaviour, modal logics are used as effective formalisms for talking about time, space, knowledge, beliefs, actions, obligations, provability, etc. However, the nice computational properties can drastically change if we combine some of these formalisms into a many-dimensional system, say, to reason about knowledge bases developing in time or moving objects.To study the computational behaviour of many-dimensional modal logics is the main aim of this book. On the one hand, it is concerned with providing a solid mathematical foundation for this discipline, while on the other hand, it shows that many seemingly different applied many-dimensional systems (e.g., multi-agent systems, description logics with epistemic, temporal and dynamic operators, spatio-temporal logics, etc.) fit in perfectly with this theoretical framework, and so their computational behaviour can be analyzed using the developed machinery.We start with concrete examples of applied one- and many-dimensional modal logics such as temporal, epistemic, dynamic, description, spatial logics, and various combinations of these. Then we develop a mathematical theory for handling a spectrum of 'abstract' combinations of modal logics - fusions and products of modal logics, fragments of first-order modal and temporal logics - focusing on three major problems: decidability, axiomatizability, and computational complexity. Besides the standard methods of modal logic, the technical toolkit includes the method of quasimodels, mosaics, tilings, reductions to monadic second-order logic, algebraic logic techniques. Finally, we apply the developed machinery and obtained results to three case studies from the field of knowledge representation and reasoning: temporal epistemic logics for reasoning about multi-agent systems, modalized description logics for dynamic ontologies, and spatio-temporal logics.The genre of the book can be defined as a research monograph. It brings the reader to the front line of current research in the field by showing both recent achievements and directions of future investigations (in particular, multiple open problems). On the other hand, well-known results from modal and first-order logic are formulated without proofs and supplied with references to accessible sources.The intended audience of this book is logicians as well as those researchers who use logic in computer science and artificial intelligence. More specific application areas are, e.g., knowledge representation and reasoning, in particular, terminological, temporal and spatial reasoning, or reasoning about agents. And we also believe that researchers from certain other disciplines, say, temporal and spatial databases or geographical information systems, will benefit from this book as well.Key Features:• Integrated approach to modern modal and temporal logics and their applications in artificial intelligence and computer science• Written by internationally leading researchers in the field of pure and applied logic• Combines mathematical theory of modal logic and applications in artificial intelligence and computer science• Numerous open problems for further research• Well illustrated with pictures and tables

    • Handbook of the Logic of Argument and Inference

      • 1st Edition
      • Volume 1
      • September 11, 2002
      • R.H. Johnson + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 4 5 4 2 1 8 2
      • eBook
        9 7 8 0 0 8 0 5 3 2 9 1 2
      The Handbook of the Logic of Argument and Inference is an authoritative reference work in a single volume, designed for the attention of senior undergraduates, graduate students and researchers in all the leading research areas concerned with the logic of practical argument and inference. After an introductory chapter, the role of standard logics is surveyed in two chapters. These chapters can serve as a mini-course for interested readers, in deductive and inductive logic, or as a refresher. Then follow two chapters of criticism; one the internal critique and the other the empirical critique. The first deals with objections to standard logics (as theories of argument and inference) arising from the research programme in philosophical logic. The second canvasses criticisms arising from work in cognitive and experimental psychology. The next five chapters deal with developments in dialogue logic, interrogative logic, informal logic, probability logic and artificial intelligence. The last chapter surveys formal approaches to practical reasoning and anticipates possible future developments. Taken as a whole the Handbook is a single-volume indication of the present state of the logic of argument and inference at its conceptual and theoretical best. Future editions will periodically incorporate significant new developments.

    • Admissibility of Logical Inference Rules

      • 1st Edition
      • Volume 136
      • March 14, 1997
      • V.V. Rybakov
      • English
      • Hardback
        9 7 8 0 4 4 4 8 9 5 0 5 9
      • Paperback
        9 7 8 0 4 4 4 5 4 6 9 8 2
      • eBook
        9 7 8 0 0 8 0 5 2 5 9 9 0
      The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on:• admissible or permissible inference rules• the derivability of the admissible inference rules• the structural completeness of logics• the bases for admissible and valid inference rules.There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered.The book is basically self-contained and special attention has been made to present the material in a convenient manner for the reader. Proofs of results, many of which are not readily available elsewhere, are also included.The book is written at a level appropriate for first-year graduate students in mathematics or computer science. Although some knowledge of elementary logic and universal algebra are necessary, the first chapter includes all the results from universal algebra and logic that the reader needs. For graduate students in mathematics and computer science the book is an excellent textbook.

    • Probabilistic Reasoning in Intelligent Systems

      • 1st Edition
      • September 1, 1988
      • Judea Pearl
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 4 7 9 7
      • eBook
        9 7 8 0 0 8 0 5 1 4 8 9 5
      Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.Probabil... Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.