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Elsevier

Morgan Kaufmann

  • Machine Learning Proceedings 1991

    Proceedings of the Eighth International Workshop (ML91)
    • 1st Edition
    • Lawrence A. Birnbaum + 1 more
    • English
    Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at Northwestern University, Evanston, Illinois, USA, in June 1991. The book focuses on constructive induction, learning from theory and data, automated knowledge acquisition, learning in intelligent information retrieval, machine learning in engineering automation, computational models of human learning, and learning reaction strategies. The selection first offers information on design rationale capture as knowledge acquisition, a domain-independent framework for effective experimentation in planning, and knowledge refinement using a high-level, non-technical vocabulary. The text then elaborates on improving the performance of inconsistent knowledge bases via combined optimization method, flexibility of speculative refinement, and a prototype based symbolic concept learning system. Topics include using task descriptions to generate error candidates, functional descriptions of knowledge-based systems, combined optimization method, and inconsistency and related work. The book ponders on learning words from context, modeling the acquisition and improvement of motor skills, a computational model of acquisition for children's addition strategies, and computer modeling of acquisition orders in child language. The manuscript also takes a look at knowledge acquisition combining analytical and empirical techniques; designing integrated learning systems for engineering design; and machine learning for nondestructive evaluation. The selection is highly recommended for researchers interested in machine learning.

  • Readings in Artificial Intelligence and Software Engineering

    • 1st Edition
    • Charles Rich + 1 more
    • English
    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.

  • Computing Perspectives

    • 1st Edition
    • Maurice V. Wilkes
    • English
    In this insightful collection of essays, Maurice Wilkes shares his unique perspective on the development of computers and the current state of the art. These enlightening essays discuss the foundational ideas behind modern computing and provide a solid grounding for the appreciation of emerging computer technologies.Wilkes, one of the founders of computing, has provided enormous contributions to the development of computers, including the design and construction of the EDSAC computer and early development of programming for a stored program computer. He was responsible for the concept of microprogramming. Wilkes also wrote the first paper to appear on cache memories and was an early worker in the field of wide bandwidth local area networks. In 1992 he was awarded the prestigious Kyoto Prize for Advanced Technology.These essays will be of interest to everyone involved with computers and how they arrived at their present state. Wilkes presents his perspectives with keen historical sensibility and engineering practicality. Readers are invited to consider these observations and form their own perspectives on the present state of the computer art.

  • Machine Learning Proceedings 1994

    Proceedings of the Eighth International Conference
    • 1st Edition
    • William W. Cohen
    • English
    Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

  • Parallel Processing from Applications to Systems

    • 1st Edition
    • Dan I. Moldovan
    • English
    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
    • Daniel E. Lenoski + 1 more
    • English
    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

    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.

  • 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.

  • 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.

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