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Morgan Kaufmann

    • Software Defined Networks

      • 1st Edition
      • May 23, 2014
      • Paul Goransson + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 4 1 6 6 7 5 2
      • eBook
        9 7 8 0 1 2 4 1 6 6 8 4 4
      Software Defined Networks discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. The book gives you the state of the art knowledge needed for successful deployment of an SDN, including: How to explain to the non-technical business decision makers in your organization the potential benefits, as well as the risks, in shifting parts of a network to the SDN model How to make intelligent decisions about when to integrate SDN technologies in a network How to decide if your organization should be developing its own SDN applications or looking to acquire these from an outside vendor How to accelerate the ability to develop your own SDN application, be it entirely novel or a more efficient approach to a long-standing problem

    • Machine Learning Proceedings 1993

      • 1st Edition
      • May 23, 2014
      • Lawrence A. Birnbaum
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 0 7 3
      • eBook
        9 7 8 1 4 8 3 2 9 8 6 2 7
      Machine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst, Massachusetts in June 27-29, 1993. The book focuses on the advancements of techniques, practices, approaches, and methodologies in machine learning. The selection first offers information on automatic algorithm/model class selection, using decision trees to improve case-based learning, GALOIS, and multitask learning. Discussions focus on multitask connectionist learning in more detail; multitask decision trees; an algorithm for the incremental determination of the concept lattice; and empirical evaluation of GALOIS as a learning system. The text then examines the use of qualitative models to guide inductive learning; automation of path analysis for building causal models from data; and construction of hidden variables in Bayesian networks via conceptual clustering. The book ponders on synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects; efficient domain-independent experimentation; learning search control knowledge for deep space network scheduling; and learning procedures from interactive natural language instructions. The selection is a dependable reference for researchers wanting to explore the field of machine learning.

    • Machine Learning Methods for Planning

      • 1st Edition
      • May 12, 2014
      • Steven Minton
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 7 4 2
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 7 5
      Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.

    • Theoretical Aspects of Reasoning About Knowledge

      • 1st Edition
      • May 12, 2014
      • Ronald Fagin
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 3 1 8
      • eBook
        9 7 8 1 4 8 3 2 1 4 5 3 5
      Theoretical Aspects of Reasoning About Knowledge contains the proceedings of the Fifth Conference on Theoretical Aspects of Reasoning About Knowledge (TARK 1994) held in Pacific Grove, California, on March 13-16, 1994. The conference provided a forum for discussing the theoretical aspects of reasoning about knowledge and tackled topics ranging from the logic of iterated belief revision and backwards forward induction to information acquisition from multi-agent resources, infinitely epistemic logic, and coherent belief revision in games. Comprised of 23 chapters, this book begins with a review of situation calculus and a solution to the frame problem, along with the use of a regression method for reasoning about the effect of actions. A novel programming language for high-level robotic control is described, along with a knowledge-based framework for belief change. Subsequent chapters deal with consistent belief reasoning in the presence of inconsistency; an epistemic logic of situations; an axiomatic approach to the logical omniscience problem; and an epistemic proof system for parallel processes. Inductive learning, knowledge asymmetries, and convention are also examined. This monograph will be of interest to both students and practitioners in the fields of artificial intelligence and computer science.

    • Readings in Fuzzy Sets for Intelligent Systems

      • 1st Edition
      • May 12, 2014
      • Didier J. Dubois + 2 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 1 4 5 0 4
      Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.

    • Connectionist Models

      • 1st Edition
      • May 12, 2014
      • David S. Touretzky + 2 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 1 5 6 7
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 8 1
      Connectionist Models contains the proceedings of the 1990 Connectionist Models Summer School held at the University of California at San Diego. The summer school provided a forum for students and faculty to assess the state of the art with regards to connectionist modeling. Topics covered range from theoretical analysis of networks to empirical investigations of learning algorithms; speech and image processing; cognitive psychology; computational neuroscience; and VLSI design. Comprised of 40 chapters, this book begins with an introduction to mean field, Boltzmann, and Hopfield networks, focusing on deterministic Boltzmann learning in networks with asymmetric connectivity; contrastive Hebbian learning in the continuous Hopfield model; and energy minimization and the satisfiability of propositional logic. Mean field networks that learn to discriminate temporally distorted strings are described. The next sections are devoted to reinforcement learning and genetic learning, along with temporal processing and modularity. Cognitive modeling and symbol processing as well as VLSI implementation are also discussed. This monograph will be of interest to both students and academicians concerned with connectionist modeling.

    • Proceedings of the Third International Conference on Data and Knowledge Bases

      • 1st Edition
      • May 12, 2014
      • C. Beeri + 2 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 1 3 1 3 2
      • eBook
        9 7 8 1 4 8 3 2 2 6 5 6 9
      Proceedings of the Third International Conference on Data and Knowledge Bases: Improving Usability and Responsiveness compiles papers presented at the Third International Conference on Data and Knowledge Bases held in Jerusalem, Israel on June 28-30, 1988. This book discusses the management system for graph-like documents, selection of processing strategies for different recursive queries, and supporting concurrent access to facts in logic programs. The design considerations for a Prolog database engine, experience with the domain algebra, and two level transaction management in a multiprocessor database machine are also described. This publication likewise covers the non-deterministic choice in Datalog and locally balanced compact Trie Hashing. This compilation is a good source for researchers and specialists of disciplines related to computer science.

    • Proceedings of the Fourth International Workshop on MACHINE LEARNING

      • 1st Edition
      • May 12, 2014
      • Pat Langley
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 4 1 5
      • eBook
        9 7 8 1 4 8 3 2 8 2 8 5 5
      Proceedings of the Fourth International Workshop 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 39 chapters, this book begins with an overview of pattern recognition systems of necessity that incorporate an approximate-matching process to determine the degree of similarity between an unknown input and all stored references. This text then describes the rationale in the Protos system for relegating inductive learning and deductive problem solving to minor roles in support of retaining, indexing and matching exemplars. Other chapters consider the power as well as the appropriateness of exemplar-based representations and their associated acquisition methods. This book discusses as well the extensions to the way a case is classified by a decision tree that address shortcomings. The final chapter deals with the advances in machine learning research. This book is a valuable resource for psychologists, scientists, theorists, and research workers.

    • Exploring Artificial Intelligence

      • 1st Edition
      • May 12, 2014
      • Howard E. Shrobe
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 6 7 5
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 5 0
      Exploring Artificial Intelligence: Survey Talks from the National Conference on Artificial Intelligence provides information pertinent to the distinct subareas of artificial intelligence research. This book discusses developments in machine learning techniques. Organized into six parts encompassing 16 chapters, this book begins with an overview of intelligent tutoring systems, which describes how to guide a student to learn new concepts. This text then links closely with one of the concerns of intelligent tutoring systems, namely how to interact through the utilization of natural language. Other chapters consider the various aspects of natural language understanding and survey the huge body of work that tries to characterize heuristic search programs. This book discusses as well how computer programs can create plans to satisfy goals. The final chapter deals with computational facilities that support. This book is a valuable resource for cognitive scientists, psychologists, domain experts, computer scientists, instructional designers, expert teachers, and research workers.

    • Readings in Artificial Intelligence

      • 1st Edition
      • May 12, 2014
      • Bonnie Lynn Webber + 1 more
      • English
      • Paperback
        9 7 8 0 9 3 4 6 1 3 0 3 3
      • eBook
        9 7 8 1 4 8 3 2 1 4 4 0 5
      Readings in Artificial Intelligence focuses on the principles, methodologies, advancements, and approaches involved in artificial intelligence. The selection first elaborates on representations of problems of reasoning about actions, a problem similarity approach to devising heuristics, and optimal search strategies for speech understanding control. Discussions focus on comparison with existing speech understanding systems, empirical comparisons of the different strategies, analysis of distance function approximation, problem similarity, problems of reasoning about action, search for solution in the reduction system, and relationship between the initial search space and the higher level search space. The book then examines consistency in networks of relations, non-resolution theorem proving, using rewriting rules for connection graphs to prove theorems, and closed world data bases. The manuscript tackles a truth maintenance system, elements of a plan-based theory of speech acts, and reasoning about knowledge and action. Topics include problems in reasoning about knowledge, integration knowledge and action, models of plans, compositional adequacy, truth maintenance mechanisms, dialectical arguments, and assumptions and the problem of control. The selection is a valuable reference for researchers wanting to explore the field of artificial intelligence.

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