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Elsevier

Morgan Kaufmann

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Uncertainty in Artificial Intelligence

    Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, UCLA, at Los Angeles, July 13-15, 1991
    • 1st Edition
    • Bruce D'Ambrosio + 2 more
    • English
    Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference (1991) covers the papers presented at the Seventh Conference on Uncertainty in Artificial Intelligence, held on July 13-15, 1991 at the University of California at Los Angeles (UCLA). The book focuses on the processes, technologies, developments, and approaches involved in artificial intelligence. The selection first offers information on combining multiple-valued logics in modular expert systems; constraint propagation with imprecise conditional probabilities; and Bayesian networks applied to therapy monitoring. The text then examines some properties of plausible reasoning; theory refinement on Bayesian networks; combination of upper and lower probabilities; and a probabilistic analysis of marker-passing techniques for plan-recognition. The publication ponders on symbolic probabilistic inference (SPI) with continuous variables, SPI with evidence potential, and local expression languages for probabilistic dependence. Topics include local expression languages for probabilistic knowledge, evidence potential algorithm, symbolic inference with evidence potential, and SPI with continuous variables algorithm. The manuscript also takes a look at the compatibility of quantitative and qualitative representations of belief and a method for integrating utility analysis into an expert system for design evaluation under uncertainty. The selection is a valuable source of data for researchers interested in artificial intelligence.

  • Cloud Networking

    Understanding Cloud-based Data Center Networks
    • 1st Edition
    • Gary Lee
    • English
    Cloud Networking: Understanding Cloud-Based Data Center Networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures, fabric technology, interconnections, and more. By the end of the book, readers will understand core networking technologies and how they’re used in a cloud data center.

  • Bridging UX and Web Development

    Better Results through Team Integration
    • 1st Edition
    • Jack Moffett
    • English
    The divide between UX and Web development can be stifling. Bridging UX and Web Development prepares you to break down those walls by teaching you how to integrate with your team’s developers. You examine the process from their perspective, discovering tools and coding principles that will help you bridge the gap between design and implementation. With these tried and true approaches, you’ll be able to capitalize on a more productive work environment. Whether you’re a novice UX professional finding your place in the software industry and looking to nail down your technical skills, or a seasoned UI designer looking for practical information on how to integrate your team with development, this is the must-have resource for your UX library.

  • Readings in Distributed Artificial Intelligence

    • 1st Edition
    • Alan H. Bond + 1 more
    • English
    Most artificial intelligence research investigates intelligent behavior for a single agent--solving problems heuristically, understanding natural language, and so on. Distributed Artificial Intelligence (DAI) is concerned with coordinated intelligent behavior: intelligent agents coordinating their knowledge, skills, and plans to act or solve problems, working toward a single goal, or toward separate, individual goals that interact. DAI provides intellectual insights about organization, interaction, and problem solving among intelligent agents. This comprehensive collection of articles shows the breadth and depth of DAI research. The selected information is relevant to emerging DAI technologies as well as to practical problems in artificial intelligence, distributed computing systems, and human-computer interaction. "Readings in Distributed Artificial Intelligence" proposes a framework for understanding the problems and possibilities of DAI. It divides the study into three realms: the natural systems approach (emulating strategies and representations people use to coordinate their activities), the engineering/science perspective (building automated, coordinated problem solvers for specific applications), and a third, hybrid approach that is useful in analyzing and developing mixed collections of machines and human agents working together. The editors introduce the volume with an important survey of the motivations, research, and results of work in DAI. This historical and conceptual overview combines with chapter introductions to guide the reader through this fascinating field. A unique and extensive bibliography is also provided.

  • Principles of Knowledge Representation and Reasoning

    Proceedings of the Fourth International Conference (KR '94)
    • 1st Edition
    • Jon Doyle + 2 more
    • English
    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.

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