Skip to main content
Elsevier
View Cart

Morgan Kaufmann

    • A Many-Sorted Calculus Based on Resolution and Paramodulation

      • 1st Edition
      • July 10, 2014
      • Christoph Walther
      • English
      • Paperback
        9 7 8 0 2 7 3 0 8 7 1 8 2
      • eBook
        9 7 8 1 4 8 3 2 5 8 9 3 5
      A Many-Sorted Calculus Based on Resolution and Paramodulation emphasizes the utilization of advantages and concepts of many-sorted logic for resolution and paramodulation based automated theorem proving. This book considers some first-order calculus that defines how theorems from given hypotheses by pure syntactic reasoning are obtained, shifting all the semantic and implicit argumentation to the syntactic and explicit level of formal first-order reasoning. This text discusses the efficiency of many-sorted reasoning, formal preliminaries for the RP- and ?RP-calculus, and many-sorted term rewriting and unification. The completeness and soundness of the ?RP-calculus, sort theorem, and automated theorem prover for the ?RP-calculus are also elaborated. This publication is a good source for students and researchers interested in many-sorted calculus.

    • Extending Explanation-Based Learning by Generalizing the Structure of Explanations

      • 1st Edition
      • July 10, 2014
      • Jude W. Shavlik
      • English
      • Paperback
        9 7 8 0 2 7 3 0 8 8 1 7 2
      • eBook
        9 7 8 1 4 8 3 2 5 8 9 1 1
      Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning.

    • Interpretation of Visual Motion

      • 1st Edition
      • July 10, 2014
      • Muralidhara Subbarao
      • English
      • Paperback
        9 7 8 0 2 7 3 0 8 7 9 2 2
      • eBook
        9 7 8 1 4 8 3 2 5 8 9 2 8
      Interpretation of Visual Motion: A Computational Study provides an information processing point of view to the phenomenon of visual motion. This book discusses the computational theory formulated for recovering the scene from monocular visual motion, determining the local geometry and rigid body motion of surfaces from spatio-temporal parameters of visual motion. This compilation also provides a theoretical and computational framework for future research on visual motion, both in human vision and machine vision areas. Other topics include the computation of image flow from intensity derivatives, instantaneous image flow due to rigid motion, time and space-time derivatives of image flow, and estimation of maximum absolute error. This publication is recommended for professionals and non-specialists intending to acquire knowledge of visual motion.

    • Design Problem Solving

      • 1st Edition
      • July 10, 2014
      • David C. Brown + 1 more
      • English
      • Paperback
        9 7 8 0 2 7 3 0 8 7 6 6 3
      • eBook
        9 7 8 1 4 8 3 2 5 8 8 8 1
      Design Problem Solving: Knowledge Structures and Control Strategies describes the application of the generic task methodology to the problem of routine design. This book discusses the generic task methodology and what constitutes the essence of the Al approach to problem solving, including the analysis of design as an information processing activity. The basic design problem solving framework, DSPL language, and AIR-CYL Air cylinder design system are also elaborated. Other topics include the high level languages based on generic tasks, structure of a Class 3 design problem solver, and failure handling in routine design. The conceptual structure for the air cylinder and improvements to DSPL system support are likewise covered in this text. This publication is beneficial to students and specialists concerned with solving design problems.

    • The Expected-Outcome Model of Two-Player Games

      • 1st Edition
      • July 10, 2014
      • Bruce Abramson
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 8 9 8 1
      • eBook
        9 7 8 1 4 8 3 2 5 8 9 5 9
      The Expected-Outcome Model of Two-Player Games deals with the expected-outcome model of two-player games, in which the relative merit of game-tree nodes, rather than board positions, is considered. The ambiguity of static evaluation and the problems it generates in the search system are examined and the development of a domain-independent static evaluator is described. Comprised of eight chapters, this book begins with an overview of the rationale for the mathematical study of games, followed by a discussion on some previous artificial intelligence (AI) research efforts on game-trees. The next section opens with the definition of a node's expected-outcome value as the expected value of the leaves beneath it. The expected-outcome model is outlined, paying particular attention to the expected-outcome value of a game-tree node. This model was implemented on some small versions of tic-tac-toe and Othello. The book also presents results that offer strong support for both the validity of the expected-outcome model and the rationality of its underlying assumptions. This monograph is intended for specialists in AI and computer science.

    • Representations of Commonsense Knowledge

      • 1st Edition
      • July 10, 2014
      • Ernest Davis
      • Ronald J. Brachman
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 7 0 4
      • eBook
        9 7 8 1 4 8 3 2 2 1 1 3 7
      Representations of Commonsense Knowledge provides a rich language for expressing commonsense knowledge and inference techniques for carrying out commonsense knowledge. This book provides a survey of the research on commonsense knowledge. Organized into 10 chapters, this book begins with an overview of the basic ideas on artificial intelligence commonsense reasoning. This text then examines the structure of logic, which is roughly analogous to that of a programming language. Other chapters describe how rules of universal validity can be applied to facts known with absolute certainty to deduce other facts known with absolute certainty. This book discusses as well some prominent issues in plausible inference. The final chapter deals with commonsense knowledge about the interrelations and interactions among agents and discusses some issues in human and social interactions that have been studied in the artificial intelligence literature. This book is a valuable resource for students on a graduate course on knowledge representation.

    • TREAT

      • 1st Edition
      • July 10, 2014
      • Daniel P. Miranker
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 8 9 9 8
      • eBook
        9 7 8 1 4 8 3 2 5 8 8 9 8
      TREAT: A New and Efficient Match Algorithm for AI Production Systems describes the architecture and software systems embodying the DADO machine, a parallel tree-structured computer designed to provide significant performance improvements over serial computers of comparable hardware complexity in the execution of large expert systems implemented in production system form. This book focuses on TREAT as a match algorithm for executing production systems that is presented and comparatively analyzed with the RETE match algorithm. TREAT, originally designed specifically for the DADO machine architecture, handles efficiently both temporally redundant and non-temporally redundant production system programs. This publication is suitable for developers and specialists interested in match algorithms for AI production systems.

    • Computer Organization and Design, Enhanced

      • 5th Edition
      • July 1, 2014
      • David A. Patterson + 1 more
      • English
      • eBook
        9 7 8 0 1 2 8 0 1 2 8 5 7
      Computer Organization and Design, Fifth Edition, moves into the post-PC era with new examples and material highlighting the emergence of mobile computing and the cloud. The book explores this generational change with updated content featuring tablet computers, cloud infrastructure, and the ARM (mobile computing devices) and x86 (cloud computing) architectures. This new edition provides in-depth coverage of parallelism with examples and content highlighting parallel hardware and software topics. It features the Intel Core i7, ARM Cortex-A8 and NVIDIA Fermi GPU as real-world examples throughout the book. It also adds a new concrete example, Going Faster, to demonstrate how understanding hardware can inspire software optimizations that improve performance by 200 times. Other topics covered include: the Eight Great Ideas of computer architecture; performance via parallelism; performance via pipelining; performance via prediction; design for Moore's Law; hierarchy of memories; abstraction to simplify design; and dependability via redundancy. The book includes a full set of updated and improved exercises as well as pop-up definitions for technical terms and concepts. Furthermore, it features interactive learning assessments that provide instant feedback in the form of true/false, multiple choice, and short essay questions. This book will appeal to professionals in computer organization and design as well as students with interest or are taking courses in this subject.

    • Machine Learning Proceedings 1989

      • 1st Edition
      • June 28, 2014
      • Alberto Maria Segre
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 3 6 2
      • eBook
        9 7 8 1 4 8 3 2 9 7 4 0 8
      Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

    • COLT '89

      • 1st Edition
      • June 28, 2014
      • COLT
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 8 6 7
      • eBook
        9 7 8 0 0 8 0 9 4 8 2 9 4
      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.

Browse by Publisher

Academic Press

Butterworth-Heinemann

Chandos

Elsevier

Newnes

Syngress