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Morgan Kaufmann

    • Uncertainty in Artificial Intelligence

      • 1st Edition
      • June 28, 2014
      • MKP
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 3 2 5
      • eBook
        9 7 8 1 4 8 3 2 9 8 6 0 3
      Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.

    • Machine Learning Proceedings 1995

      • 1st Edition
      • June 28, 2014
      • Armand Prieditis + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 7 7 6
      • eBook
        9 7 8 1 4 8 3 2 9 8 6 6 5
      Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

    • Reasoning About Plans

      • 1st Edition
      • June 28, 2014
      • James Allen + 3 more
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 1 3 8
      • eBook
        9 7 8 1 4 8 3 2 9 5 9 6 1
      This book presents four contributions to planning research within an integrated framework. James Allen offers a survey of his research in the field of temporal reasoning, and then describes a planning system formalized and implemented directly as an inference process in the temporal logic. Starting from the same logic, Henry Kautz develops the first formal specification of the plan recognition process and develops a powerful family of algorithms for plan recognition in complex situations. Richard Pelavin then extends the temporal logic with model operators that allow the representation to support reasoning about complex planning situations involving simultaneous interacting actions, and interaction with external events. Finally, Josh Tenenberg introduces two different formalisms of abstraction in planning systems and explores the properties of these abstraction techniques in depth.

    • 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.

    • Parallel Processing from Applications to Systems

      • 1st Edition
      • June 28, 2014
      • Dan I. Moldovan
      • English
      • eBook
        9 7 8 1 4 8 3 2 9 7 5 1 4
      This text provides one of the broadest presentations of parallelprocessing available, including the structure of parallelprocessors and parallel algorithms. The emphasis is on mappingalgorithms to highly parallel computers, with extensive coverage ofarray and multiprocessor architectures. Early chapters provideinsightful coverage on the analysis of parallel algorithms andprogram transformations, effectively integrating a variety ofmaterial previously scattered throughout the literature. Theory andpractice are well balanced across diverse topics in this concisepresentation. For exceptional clarity and comprehension, the authorpresents complex material in geometric graphs as well as algebraicnotation. Each chapter includes well-chosen examples, tablessummarizing related key concepts and definitions, and a broad rangeof worked exercises.

    • Scalable Shared-Memory Multiprocessing

      • 1st Edition
      • June 28, 2014
      • Daniel E. Lenoski + 1 more
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 1 4 5
      • eBook
        9 7 8 1 4 8 3 2 9 6 0 1 2
      Dr. Lenoski and Dr. Weber have experience with leading-edge research and practical issues involved in implementing large-scale parallel systems. They were key contributors to the architecture and design of the DASH multiprocessor. Currently, they are involved with commercializing scalable shared-memory technology.

    • Representation and Understanding

      • 1st Edition
      • June 28, 2014
      • Jerry Bobrow
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 0 0 8
      • eBook
        9 7 8 1 4 8 3 2 9 9 1 5 0
      Language, Thought, and Culture: Advances in the Study of Cognition: Representation and Understanding: Studies in Cognitive Science focuses on the principles, processes, and methodologies involved in artificial intelligence. The selection first offers information on the dimensions of representation, foundations for semantic networks, and reflections on the formal description of behavior. Discussions focus on relativity of behavioral description, hierarchical organization of processes, problems in knowledge representation, and inference, access, and self-awareness. The text then takes a look at the synthesis, analysis, and contingent knowledge in specialized understanding systems, some principles of memory schemata, and representing knowledge for recognition. The book examines frame representations and declarative/procedur... controversy, schema for stories, and structure of episodes in memory. Topics include long-term memory, conceptual dependency, understanding paragraphs, simple story grammar, and first attempt at synthesis. The publication then ponders on concepts for representing mundane reality in plans and multiple representations of knowledge for tutorial reasoning. The selection is highly recommended for researchers interested in exploring artificial intelligence.

    • Principles of Artificial Intelligence

      • 1st Edition
      • June 28, 2014
      • Nils J. Nilsson
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 0 6 0
      • eBook
        9 7 8 1 4 8 3 2 9 5 8 6 2
      A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

    • Readings in Artificial Intelligence and Databases

      • 1st Edition
      • June 28, 2014
      • John Mylopoulos + 1 more
      • English
      • eBook
        9 7 8 0 0 8 0 8 8 6 6 2 6
      The interaction of database and AI technologies is crucial to such applications as data mining, active databases, and knowledge-based expert systems. This volume collects the primary readings on the interactions, actual and potential, between these two fields. The editors have chosen articles to balance significant early research and the best and most comprehensive articles from the 1980s. An in-depth introduction discusses basic research motivations, giving a survey of the history, concepts, and terminology of the interaction. Major themes, approaches and results, open issues and future directions are all discussed, including the results of a major survey conducted by the editors of current work in industry and research labs. Thirteen sections follow, each with a short introduction.Topics examined include semantic data models with emphasis on conceptual modeling techniques for databases and information systems and the integration of data model concepts in high-level data languages, definition and maintenance of integrity constraints in databases and knowledge bases, natural language front ends, object-oriented database management systems, implementation issues such as concurrency control and error recovery, and representation of time and knowledge incompleteness from the viewpoints of databases, logic programming, and AI.

    • Readings in Artificial Intelligence and Software Engineering

      • 1st Edition
      • June 28, 2014
      • Charles Rich + 1 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 2 9
      Readings in Artificial Intelligence and Software Engineering covers the main techniques and application of artificial intelligence and software engineering. The ultimate goal of artificial intelligence applied to software engineering is automatic programming. Automatic programming would allow a user to simply say what is wanted and have a program produced completely automatically. This book is organized into 11 parts encompassing 34 chapters that specifically tackle the topics of deductive synthesis, program transformations, program verification, and programming tutors. The opening parts provide an introduction to the key ideas to the deductive approach, namely the correspondence between theorems and specifications and between constructive proofs and programs. These parts also describes automatic theorem provers whose development has be designed for the programming domain. The subsequent parts present generalized program transformation systems, the problems involved in using natural language input, the features of very high level languages, and the advantages of the programming by example system. Other parts explore the intelligent assistant approach and the significance and relation of programming knowledge in other programming system. The concluding parts focus on the features of the domain knowledge system and the artificial intelligence programming. Software engineers and designers and computer programmers, as well as researchers in the field of artificial intelligence will find this book invaluable.

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