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Morgan Kaufmann

  • Readings in Computer Vision

    Issues, Problem, Principles, and Paradigms
    • 1st Edition
    • Martin A. Fischler + 1 more
    • English
    The field of computer vision combines techniques from physics, mathematics, psychology, artificial intelligence, and computer science to examine how machines might construct meaningful descriptions of their surrounding environment. The editors of this volume, prominent researchers and leaders of the SRI International AI Center Perception Group, have selected sixty papers, most published since 1980, with the viewpoint that computer vision is concerned with solving seven basic problems:Reconstruct... 3D scenes from 2D imagesDecomposing images into their component partsRecognizing and assigning labels to scene objectsDeducing and describing relations among scene objectsDetermining the nature of computer architectures that can support the visual functionRepresenting abstractions in the world of computer memoryMatching stored descriptions to image representationEach chapter of this volume addresses one of these problems through an introductory discussion, which identifies major ideas and summarizes approaches, and through reprints of key research papers. Two appendices on crucial assumptions in image interpretation and on parallel architectures for vision applications, a glossary of technical terms, and a comprehensive bibliography and index complete the volume.

  • COLT '89

    Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989
    • 1st Edition
    • COLT
    • English
    Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.

  • Readings in Human-Computer Interaction

    Toward the Year 2000
    • 1st Edition
    • Ronald M. Baecker
    • English
    The effectiveness of the user-computer interface has become increasingly important as computer systems have become useful tools for persons not trained in computer science. In fact, the interface is often the most important factor in the success or failure of any computer system. Dealing with the numerous subtly interrelated issues and technical, behavioral, and aesthetic considerations consumes a large and increasing share of development time and a corresponding percentage of the total code for any given application. A revision of one of the most successful books on human-computer interaction, this compilation gives students, researchers, and practitioners an overview of the significant concepts and results in the field and a comprehensive guide to the research literature. Like the first edition, this book combines reprints of key research papers and case studies with synthesizing survey material and analysis by the editors. It is significantly reorganized, updated, and enhanced; over 90% of the papers are new. An invaluable resource for systems designers, cognitive scientists, computer scientists, managers, and anyone concerned with the effectiveness of user-computer interfaces, it is also designed for use as a primary or supplementary text for graduate and advanced undergraduate courses in human-computer interaction and interface design.

  • Readings in Artificial Intelligence and Databases

    • 1st Edition
    • John Mylopoulos + 1 more
    • English
    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.

  • Reasoning About Plans

    • 1st Edition
    • James Allen + 3 more
    • English
    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.

  • Machine Learning Proceedings 1995

    Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995
    • 1st Edition
    • Armand Prieditis + 1 more
    • English
    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.

  • Principles of Artificial Intelligence

    • 1st Edition
    • Nils J. Nilsson
    • English
    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.

  • Representation and Understanding

    Studies in Cognitive Science
    • 1st Edition
    • Jerry Bobrow
    • English
    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.

  • Machine Learning

    An Artificial Intelligence Approach, Volume III
    • 1st Edition
    • Yves Kodratoff + 1 more
    • English
    Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

  • Case-Based Reasoning

    • 1st Edition
    • Janet Kolodner
    • English
    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.

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