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Books in Artificial intelligence

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491-500 of 523 results in All results

Computers and Conversation

  • 1st Edition
  • January 28, 1990
  • Paul Luff + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 0 2 6 4 - 9
In the past few years a branch of sociology, conversation analysis, has begun to have a significant impact on the design of human*b1computer interaction (HCI). The investigation of human*b1human dialogue has emerged as a fruitful foundation for interactive system design.****This book includes eleven original chapters by leading researchers who are applying conversation analysis to HCI. The fundamentals of conversation analysis are outlined, a number of systems are described, and a critical view of their value for HCI is offered.****Computers and Conversation will be of interest to all concerned with HCI issues--from the advanced student to the professional computer scientist involved in the design and specification of interactive systems.

COLT '89

  • 1st Edition
  • December 25, 1989
  • COLT
  • English
  • 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.

Stimulus-Response Compatibility

  • 1st Edition
  • Volume 65
  • December 18, 1989
  • R.W. Proctor + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 6 7 1 9 - 9
Stimulus-response compatibility refers to the finding that certain mappings of stimuli to responses produce faster and more accurate responding than do others. The present volume surveys compatibility research which falls into four broad categories: (a) mental representation and coding (b) neurophysiological mechanisms (c) motor performance (d) human factors applications. The major findings and models within each of the categories are summarized, and an integrated perspective is provided. The research indicates that compatibility effects reflect basic cognitive processes that bear on a range of issues in cognitive science and that have applied implications for human factors specialists.

Connectionism in Perspective

  • 1st Edition
  • August 23, 1989
  • R. Pfeifer + 3 more
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 8 7 6 - 9
An evaluation of the merits, potential, and limits of Connectionism, this book also illustrates current research programs and recent trends.Connectionism (also known as Neural Networks) is an exciting new field which has brought together researchers from different areas such as artificial intelligence, computer science, cognitive science, neuroscience, physics, and complex dynamics. These researchers are applying the connectionist paradigm in an interdisciplinary way to the analysis and design of intelligent systems.In this book, researchers from the above-mentioned fields not only report on their most recent research results, but also describe Connectionism from the perspective of their own field, looking at issues such as: - the effects and the utility of Connectionism for their field - the potential and limitations of Connectionism - can it be combined with other approaches?

Machine Learning Proceedings 1989

  • 1st Edition
  • June 1, 1989
  • Alberto Maria Segre
  • English
  • 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.

Knowledge Acquisition from Text and Pictures

  • 1st Edition
  • Volume 58
  • April 1, 1989
  • H. Mandl + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 6 7 1 2 - 0
Media-didactics have recently become more firmly grounded on cognitive theory, with an increasing concern for the internal processes of knowledge representation and acquisition. With this cognitive aspect in mind, an international group of researchers held a meeting in Tübingen, Federal Republic of Germany, to present and discuss the theoretical approaches to and empirical investigations of knowledge acquisition from text and pictures. This volume contains the revised contributions resulting from that meeting.

Introduction to Machine Learning

  • 1st Edition
  • March 1, 1989
  • Yves Kodratoff
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 0 9 3 0 - 3
A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject.Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginning with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.

Parallel Processing for Artificial Intelligence 1

  • 1st Edition
  • Volume 14
  • February 12, 1989
  • L.N. Kanal + 3 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 7 4 - 9
Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discusses parallel processing for semantic networks, which are widely used means for representing knowledge - methods which enable efficient and flexible processing of semantic networks are expected to have high utility for building large-scale knowledge-based systems. The third section explores the automatic parallel execution of production systems, which are used extensively in building rule-based expert systems - systems containing large numbers of rules are slow to execute and can significantly benefit from automatic parallel execution. The exploitation of parallelism for the mechanization of logic is dealt with in the fourth section. While sequential control aspects pose problems for the parallelization of production systems, logic has a purely declarative interpretation which does not demand a particular evaluation strategy. In this area, therefore, very large search spaces provide significant potential for parallelism. In particular, this is true for automated theorem proving. The fifth section considers the problem of constraint satisfaction, which is a useful abstraction of a number of important problems in AI and other fields of computer science. It also discusses the technique of consistent labeling as a preprocessing step in the constraint satisfaction problem. Section VI consists of two articles, each on a different, important topic. The first discusses parallel formulation for the Tree Adjoining Grammar (TAG), which is a powerful formalism for describing natural languages. The second examines the suitability of a parallel programming paradigm called Linda, for solving problems in artificial intelligence.Each of the areas discussed in the book holds many open problems, but it is believed that parallel processing will form a key ingredient in achieving at least partial solutions. It is hoped that the contributions, sourced from experts around the world, will inspire readers to take on these challenging areas of inquiry.

Topics in Expert System Design

  • 1st Edition
  • Volume 5
  • February 12, 1989
  • C. Tasso + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 7 7 - 4
Expert Systems are so far the most promising achievement of artificial intelligence research. Decision making, planning, design, control, supervision and diagnosis are areas where they are showing great potential. However, the establishment of expert system technology and its actual industrial impact are still limited by the lack of a sound, general and reliable design and construction methodology.This book has a dual purpose: to offer concrete guidelines and tools to the designers of expert systems, and to promote basic and applied research on methodologies and tools. It is a coordinated collection of papers from researchers in the USA and Europe, examining important and emerging topics, methodological advances and practical experience obtained in specific applications. Each paper includes a survey introduction, and a comprehensive bibliography is provided.

Machine Learning Proceedings 1988

  • 1st Edition
  • December 25, 1988
  • John Laird
  • English
  • 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.

Related subjects

Artificial intelligence expert systems and knowledge based systems

Artificial intelligence general

Cognitive science

Computer human interaction

Evolutionary computation genetic algorithms programming

Fuzzy logic

Machine learning

Natural language and computational linguistics

Neural networks

Planning and scheduling

Reasoning

Robotics and autonomous systems