Skip to main content
Elsevier

Morgan Kaufmann

  • Machine Learning Methods for Planning

    • 1st Edition
    • Steven Minton
    • English
    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.

  • Uncertainty in Artificial Intelligence

    Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C. 1993
    • 1st Edition
    • David Heckerman + 1 more
    • English
    Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

  • Proceedings of the Fourth International Workshop on MACHINE LEARNING

    June 22–25, 1987 University of California, Irvine
    • 1st Edition
    • Pat Langley
    • English
    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.

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

    Improving Usability and Responsiveness
    • 1st Edition
    • C. Beeri + 2 more
    • English
    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.

  • Theoretical Aspects of Reasoning About Knowledge

    Proceedings of the Fifth Conference (TARK 1994)
    • 1st Edition
    • Ronald Fagin
    • English
    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.

  • Artificial Intelligence and Tutoring Systems

    Computational and Cognitive Approaches to the Communication of Knowledge
    • 1st Edition
    • Etienne Wenger
    • English
    Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge focuses on the cognitive approaches, methodologies, principles, and concepts involved in the communication of knowledge. The publication first elaborates on knowledge communication systems, basic issues, and tutorial dialogues. Concerns cover natural reasoning and tutorial dialogues, shift from local strategies to multiple mental models, domain knowledge, pedagogical knowledge, implicit versus explicit encoding of knowledge, knowledge communication, and practical and theoretical implications. The text then examines interactive simulations, existing CAI traditions, and learning environments. The manuscript elaborates on knowledge communication, didactics, and diagnosis. Topics include knowledge presentation and communication, pedagogical contexts, target levels of didactic operations, behavioral and epistemic diagnosis, and aspects of diagnostic experience. The publication is a dependable reference for researchers interested in the computational and cognitive approaches to the communication of knowledge.

  • Foundations of Deductive Databases and Logic Programming

    • 1st Edition
    • Jack Minker
    • English
    Foundations of Deductive Databases and Logic Programming focuses on the foundational issues concerning deductive databases and logic programming. The selection first elaborates on negation in logic programming and towards a theory of declarative knowledge. Discussions focus on model theory of stratified programs, fixed point theory of nonmonotonic operators, stratified programs, semantics for negation in terms of special classes of models, relation between closed world assumption and the completed database, negation as a failure, and closed world assumption. The book then takes a look at negation as failure using tight derivations for general logic programs, declarative semantics of logic programs with negation, and declarative semantics of deductive databases and logic programs. The publication tackles converting AND-control to OR-control by program transformation, optimizing dialog, equivalences of logic programs, unification, and logic programming and parallel complexity. Topics include parallelism and structured and unstructured data, parallel algorithms and complexity, solving equations, most general unifiers, systems of equations and inequations, equivalences of logic programs, and optimizing recursive programs. The selection is a valuable source of data for researchers interested in pursuing further studies on the foundations of deductive databases and logic programming.

  • Connectionist Models

    Proceedings of the 1990 Summer School
    • 1st Edition
    • David S. Touretzky + 2 more
    • English
    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.

  • Uncertainty in Artificial Intelligence

    Proceedings of the Eighth Conference (1992), July 17–19, 1992, Eighth Conference on Uncertainty in Artificial Intelligence, Stanford University
    • 1st Edition
    • Didier J. Dubois + 2 more
    • English
    Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

  • Theoretical Aspects of Reasoning About Knowledge

    Proceedings of the 1986 Conference
    • 1st Edition
    • Joseph Y. Halpern
    • English
    Theoretical Aspects of Reasoning About Knowledge: Proceedings of the 1986 Conference focuses on the principles, methodologies, approaches, and concepts involved in reasoning about knowledge. The selection first provides an overview of reasoning about knowledge, varieties of self-reference, and pegs and alecs. Topics covered include data semantics, partial objects and identity, circumstance, self, and causal connection, structure of circumstance, varieties and limits of self-reference, problem of logical omniscience, and knowledge, communication, and action. The book then explores reasoning about knowledge in artificial intelligence; synthesis of digital machines with provable epistemic properties; and a first order theory of planning, knowledge, and action. The publication ponders on the consistency of syntactical treatments of knowledge, foundations of knowledge for distributed systems, knowledge and implicit knowledge in a distributed environment, and the logic of distributed protocols. Topics include formal syntax and semantics, structure of models, message-based knowledge worlds, changing the class of messages, implicit knowledge in message-based knowledge worlds, conservation and implicit knowledge, and distributed protocols. The selection is a dependable source of data for researchers interested in the theoretical aspects of reasoning about knowledge.

Browse by Publisher