Books in Artificial intelligence expert systems and knowledge based systems

101-110 of 149 results in All results

Machine Learning Proceedings 1995

• 1st Edition
• July 1, 1995
• Armand Prieditis + 1 more
• English
• eBook 978-1-4832-9866-5

  9 7 8 - 1 - 4 8 3 2 - 9 8 6 6 - 5

Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

Introduction to Knowledge Systems

• 1st Edition
• June 1, 1995
• Mark Stefik
• English
• eBook 978-0-08-050916-7

  9 7 8 - 0 - 0 8 - 0 5 0 9 1 6 - 7

Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical. Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis. The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications. Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.

Uncertainty in Artificial Intelligence

• 1st Edition
• July 1, 1994
• MKP
• English
• eBook 978-1-4832-9860-3

  9 7 8 - 1 - 4 8 3 2 - 9 8 6 0 - 3

Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.

Machine Learning Proceedings 1994

• 1st Edition
• July 1, 1994
• William W. Cohen
• English
• eBook 978-1-4832-9818-4

  9 7 8 - 1 - 4 8 3 2 - 9 8 1 8 - 4

Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

Principles of Knowledge Representation and Reasoning

• 1st Edition
• June 8, 1994
• Jon Doyle + 2 more
• English
• eBook 978-1-4832-1452-8

  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.

Time-Varying Image Processing and Moving Object Recognition

• 1st Edition
• March 17, 1994
• V. Cappellini
• English
• Hardback 978-0-444-81467-8

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• eBook 978-1-4832-9025-6

  9 7 8 - 1 - 4 8 3 2 - 9 0 2 5 - 6

In the area of Digital Image Processing the new area of "Time-Varying Image Processing and Moving Oject Recognition" is contributing to impressive advances in several fields. Presented in this volume are new digital image processing and recognition methods, implementation techniques and advanced applications such as television, remote sensing, biomedicine, traffic, inspection, and robotics. New approaches (such as digital transforms, neural networks) for solving 2-D and 3-D problems are described. Many papers concentrate on motion estimation and recognition i.e. tracking of moving objects. Overall, the book describes the state-of-the-art (theory, implementation, applications) of this developing area, together with future trends. The work will be of interest not only to researchers, professors and students in university departments of engineering, communications, computers and automatic control, but also to engineers and managers of industries concerned with computer vision, manufacturing, automation, robotics and quality control.

Foundations of Genetic Algorithms 1995 (FOGA 3)

• 1st Edition
• Volume 3
• March 7, 1994
• FOGA
• English
• eBook 978-1-4832-9502-2

  9 7 8 - 1 - 4 8 3 2 - 9 5 0 2 - 2

Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms. The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-independent recombination, representation-independent mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms. The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata. The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.

Mathematical Approaches to Neural Networks

• 1st Edition
• Volume 51
• October 27, 1993
• J.G. Taylor
• English
• eBook 978-0-08-088739-5

  9 7 8 - 0 - 0 8 - 0 8 8 7 3 9 - 5

The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing.This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.

Intelligent Software for Chemical Analysis

• 1st Edition
• Volume 13
• September 3, 1993
• L.M.C. Buydens + 1 more
• English
• eBook 978-0-08-086840-0

  9 7 8 - 0 - 0 8 - 0 8 6 8 4 0 - 0

Various emerging techniques for automating intelligent functions in the laboratory are described in this book. Explanations on how systems work are given and possible application areas are suggested. The main part of the book is devoted to providing data which will enable the reader to develop and test his own systems. The emphasis is on expert systems; however, promising developments such as self-adaptive systems, neural networks and genetic algorithms are also described. The book has been written by chemists with a great deal of practical experience in developing and testing intelligent software, and therefore offers first-hand knowledge. Laboratory staff and managers confronted with commercial intelligent software will find information on the functioning, possibilities and limitations thereof, enabling them to select and use modern software in an optimum fashion. Finally, computer scientists and information scientists will find a wealth of data on the application of contemporary artificial intelligence techniques.

Case-Based Reasoning

• 1st Edition
• September 1, 1993
• Janet Kolodner
• English
• eBook 978-1-4832-9449-0

  9 7 8 - 1 - 4 8 3 2 - 9 4 4 9 - 0

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.