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Morgan Kaufmann

    • Concept Formation

      • 1st Edition
      • May 12, 2014
      • Douglas H. Fisher + 2 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 7 3 5
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 6 8
      Concept Formation: Knowledge and Experience in Unsupervised Learning presents the interdisciplinary interaction between machine learning and cognitive psychology on unsupervised incremental methods. This book focuses on measures of similarity, strategies for robust incremental learning, and the psychological consistency of various approaches. Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and from numeric and conceptual clustering. This text then describes the cognitive consistency of two concept formation systems that are motivated by a rational analysis of human behavior relative to a variety of psychological phenomena. Other chapters consider the merits of various schemes for representing and acquiring knowledge during concept formation. This book discusses as well the earliest work in concept formation. The final chapter deals with acquisition of quantity conservation in developmental psychology. This book is a valuable resource for psychologists and cognitive scientists.

    • Uncertainty in Artificial Intelligence

      • 1st Edition
      • May 12, 2014
      • David Heckerman + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 0 6 6
      • eBook
        9 7 8 1 4 8 3 2 1 4 5 1 1
      Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

    • The Frame Problem in Artificial Intelligence

      • 1st Edition
      • May 12, 2014
      • Frank M. Brown
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 3 2 3
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 3 6
      The Frame Problem in Artificial Intelligence: Proceedings of the 1987 Workshop focuses on the approaches, principles, and concepts related to the frame problem in artificial intelligence (AI). The selection first tackles the definition of the frame problem, circumscription approaches and criticisms, modal logic approaches, and syntactic consistency approaches. The text then takes a look at two frame problems, frame problem in AI, and the frame problem in AI histories, including frame problem defined, mathematical frame problem, commonsense frame problem, and the problems of qualification and extended prediction and their relation to the frame problem. The publication examines tense-logic-based mitigation of the frame problem, unframing the frame problem, a truth maintenance based approach to the frame problem, and qualification problem. Topics include possible worlds, qualification and possible worlds, epistemological issues, truth maintenance, contradiction handling, application of intensional logic, development and implementation of chronolog, and approaches to solving the frame problem. The selection is a dependable source of data for researchers interested in the frame problem.

    • Theoretical Aspects of Reasoning About Knowledge

      • 1st Edition
      • May 12, 2014
      • Joseph Y. Halpern
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 0 4 0
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 1 2
      Theoretical Aspects of Reasoning About Knowledge: Proceedings of the 1986 Conference focuses on the principles, methodologies, approaches, and concepts involved in reasoning about knowledge. The selection first provides an overview of reasoning about knowledge, varieties of self-reference, and pegs and alecs. Topics covered include data semantics, partial objects and identity, circumstance, self, and causal connection, structure of circumstance, varieties and limits of self-reference, problem of logical omniscience, and knowledge, communication, and action. The book then explores reasoning about knowledge in artificial intelligence; synthesis of digital machines with provable epistemic properties; and a first order theory of planning, knowledge, and action. The publication ponders on the consistency of syntactical treatments of knowledge, foundations of knowledge for distributed systems, knowledge and implicit knowledge in a distributed environment, and the logic of distributed protocols. Topics include formal syntax and semantics, structure of models, message-based knowledge worlds, changing the class of messages, implicit knowledge in message-based knowledge worlds, conservation and implicit knowledge, and distributed protocols. The selection is a dependable source of data for researchers interested in the theoretical aspects of reasoning about knowledge.

    • Database

      • 1st Edition
      • May 12, 2014
      • Patrick O'Neil
      • English
      • eBook
        9 7 8 1 4 8 3 1 8 4 0 4 3
      Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance. Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programming language. This book provides as well an introduction to Embedded SQL and Dynamic SQL that is sufficiently detailed to enable students to immediately start writing database programs. The final chapter deals with some of the motivations for database systems spanning multiple CPUs, including client-server and distributed transactions. This book is a valuable resource for database administrators, application programmers, specialist users, and end users.

    • Artificial Intelligence and Tutoring Systems

      • 1st Edition
      • May 12, 2014
      • Etienne Wenger
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 6 8 1
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 1 3
      Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge focuses on the cognitive approaches, methodologies, principles, and concepts involved in the communication of knowledge. The publication first elaborates on knowledge communication systems, basic issues, and tutorial dialogues. Concerns cover natural reasoning and tutorial dialogues, shift from local strategies to multiple mental models, domain knowledge, pedagogical knowledge, implicit versus explicit encoding of knowledge, knowledge communication, and practical and theoretical implications. The text then examines interactive simulations, existing CAI traditions, and learning environments. The manuscript elaborates on knowledge communication, didactics, and diagnosis. Topics include knowledge presentation and communication, pedagogical contexts, target levels of didactic operations, behavioral and epistemic diagnosis, and aspects of diagnostic experience. The publication is a dependable reference for researchers interested in the computational and cognitive approaches to the communication of knowledge.

    • Computer Organization and Design

      • 1st Edition
      • May 12, 2014
      • John L. Hennessy + 1 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 8 2
      Computer Organization and Design: The Hardware/Software Interface presents the interaction between hardware and software at a variety of levels, which offers a framework for understanding the fundamentals of computing. This book focuses on the concepts that are the basis for computers. Organized into nine chapters, this book begins with an overview of the computer revolution. This text then explains the concepts and algorithms used in modern computer arithmetic. Other chapters consider the abstractions and concepts in memory hierarchies by starting with the simplest possible cache. This book discusses as well the complete data path and control for a processor. The final chapter deals with the exploitation of parallel machines. This book is a valuable resource for students in computer science and engineering. Readers with backgrounds in assembly language and logic design who want to learn how to design a computer or understand how a system works will also find this book useful.

    • Uncertainty in Artificial Intelligence

      • 1st Edition
      • May 12, 2014
      • Didier J. Dubois + 2 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 5 8 8
      • eBook
        9 7 8 1 4 8 3 2 8 2 8 7 9
      Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

    • Introduction to Parallel Algorithms and Architectures

      • 1st Edition
      • May 12, 2014
      • F. Thomson Leighton
      • English
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 5 1
      Introduction to Parallel Algorithms and Architectures: Arrays Trees Hypercubes provides an introduction to the expanding field of parallel algorithms and architectures. This book focuses on parallel computation involving the most popular network architectures, namely, arrays, trees, hypercubes, and some closely related networks. Organized into three chapters, this book begins with an overview of the simplest architectures of arrays and trees. This text then presents the structures and relationships between the dominant network architectures, as well as the most efficient parallel algorithms for a wide variety of problems. Other chapters focus on fundamental results and techniques and on rigorous analysis of algorithmic performance. This book discusses as well a hybrid of network architecture based on arrays and trees called the mesh of trees. The final chapter deals with the most important properties of hypercubes. This book is a valuable resource for readers with a general technical background.

    • Foundations of Deductive Databases and Logic Programming

      • 1st Edition
      • May 12, 2014
      • Jack Minker
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 6 9 8
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 2 0
      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.

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