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Elsevier

Morgan Kaufmann

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Economics-Driven Software Architecture

    • 1st Edition
    • Ivan Mistrik + 3 more
    • English
    Economics-driven Software Architecture presents a guide for engineers and architects who need to understand the economic impact of architecture design decisions: the long term and strategic viability, cost-effectiveness, and sustainability of applications and systems. Economics-driven software development can increase quality, productivity, and profitability, but comprehensive knowledge is needed to understand the architectural challenges involved in dealing with the development of large, architecturally challenging systems in an economic way. This book covers how to apply economic considerations during the software architecting activities of a project. Architecture-centric approaches to development and systematic evolution, where managing complexity, cost reduction, risk mitigation, evolvability, strategic planning and long-term value creation are among the major drivers for adopting such approaches. It assists the objective assessment of the lifetime costs and benefits of evolving systems, and the identification of legacy situations, where architecture or a component is indispensable but can no longer be evolved to meet changing needs at economic cost. Such consideration will form the scientific foundation for reasoning about the economics of nonfunctional requirements in the context of architectures and architecting.

  • Machine Learning Proceedings 1988

    • 1st Edition
    • John Laird
    • English
    Proceedings of the Fifth International Conference on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition. Organized into 10 parts encompassing 49 chapters, this book begins with an overview of the OTIS induction system that learns concepts from positive and negative examples by searching through the space of possible concept descriptions. This text then reviews the methods to selecting examples, and explores the ramifications of one in detail. Other chapters consider a reported phenomenon in machine concept learning wherein concept descriptions can be simplified with little ill-effect on classification accuracy. This book discusses as well an implemented system that learns structural models of shape from noisy image data. The final chapter provides a discussion of the relationship between learning and forgetting. This book is a valuable resource for psychologists, scientists, theorists, and research workers.

  • Distributed Artificial Intelligence

    Volume II
    • 1st Edition
    • Robin Gasser + 1 more
    • English
    Research Notes in Artificial Intelligence: Distributed Artificial Intelligence, Volume II focuses on the growing interest in Distributed Artificial Intelligence (DAI). The selection first offers information on a unified theory of communication and social structure and boundary objects and heterogeneous distributed problem solving. Discussions focus on types of boundary objects, heterogeneous problem solving and boundary objects, social structures and social groups, and social cooperation and communication. The text then examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. The publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource reallocations, and plans for multiple agents. Topics include plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript then elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning. The selection is a valuable source of information for researchers interested in distributed artificial intelligence.

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