Skip to main content
Elsevier
View Cart

Morgan Kaufmann

    • Uncertainty in Artificial Intelligence

      • 1st Edition
      • June 28, 2014
      • Bruce D'Ambrosio + 2 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 9 8 5 6 6
      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

      • 1st Edition
      • June 9, 2014
      • Gary Lee
      • English
      • Paperback
        9 7 8 0 1 2 8 0 0 7 2 8 0
      • eBook
        9 7 8 0 1 2 8 0 0 8 1 6 4
      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

      • 1st Edition
      • June 6, 2014
      • Jack Moffett
      • English
      • Paperback
        9 7 8 0 1 2 4 2 0 2 4 5 0
      • eBook
        9 7 8 0 1 2 8 0 0 7 8 7 7
      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

      • 1st Edition
      • June 5, 2014
      • Jon Doyle + 2 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 2 8 8
      • eBook
        9 7 8 1 4 8 3 2 1 4 5 2 8
      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
      • June 5, 2014
      • Alan H. Bond + 1 more
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 6 3 7
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 4 3
      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
      • June 3, 2014
      • Ivan Mistrik + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 4 1 0 4 6 4 8
      • eBook
        9 7 8 0 1 2 4 1 0 5 0 7 2
      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.

    • Distributed Artificial Intelligence

      • 1st Edition
      • May 23, 2014
      • Robin Gasser + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 9 2 8
      • eBook
        9 7 8 1 4 8 3 2 9 4 8 1 0
      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.

    • Machine Learning Proceedings 1990

      • 1st Edition
      • May 23, 2014
      • Bruce Porter + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 1 4 1 3
      • eBook
        9 7 8 1 4 8 3 2 9 8 5 8 0
      Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin. The book focuses on the progress in the interest in machine learning, including methodologies, approaches, and techniques. The selection first offers information on knowledge acquisition from examples using maximal representation learning, performance analysis of a probabilistic inductive learning system, and a comparative study of ID3 and backpropagation for English text-to-speech mapping. The text then examines learning from data with bounded inconsistency, improving fit-and-split algorithms, and an incremental method for finding multivariate splits for decision trees. Topics include issues for decision-tree induction, learning and approximation, conceptual-set-cover... algorithm, bounded inconsistency, implementation, and examples of incremental processes. The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning. Learning procedures by environment-driven constructive induction and improving the performance of genetic algorithms in automated discovery of parameters are also discussed. The selection is a valuable source of data for researchers interested in machine learning.

    • COLT '91

      • 1st Edition
      • May 23, 2014
      • COLT
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 1 3 7
      • eBook
        9 7 8 1 4 8 3 2 9 9 1 4 3
      COLT '91: Proceedings of the Fourth Annual Workshop on Computational Learning Theory covers the papers presented at the Fourth Workshop on Computational Learning Theory, held at the University of California at Santa Cruz on August 5-7, 1991. The book focuses on quantitative theories of machine learning. The selection first offers information on the role of learning in autonomous robots; tracking drifting concepts using random examples; investigating the distribution assumptions in the PAC learning model; and simultaneous learning of concepts and simultaneous estimation of probabilities.The text then examines the calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise and a geometric approach to threshold circuit complexity. The manuscript takes a look at learning curves in large neural networks, learnability of infinitary regular sets, and learning monotone DNF with an incomplete membership oracle. Topics include monotone DNF learning algorithm, difficulties in learning infinitary regular sets, learning of a perception rule, and annealed approximation. The book also examines the fast identification of geometric objects with membership queries and a loss bound model for on-line stochastic prediction strategies. The selection is a valuable source of information for researchers interested in the computational learning theory.

    • Machine Learning Proceedings 1988

      • 1st Edition
      • May 23, 2014
      • John Laird
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 6 4 4
      • eBook
        9 7 8 1 4 8 3 2 9 7 6 9 9
      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.

Browse by Publisher

Academic Press

Butterworth-Heinemann

Chandos

Elsevier

Newnes

Syngress