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Books in Artificial intelligence

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Neural Network PC Tools

  • 1st Edition
  • October 28, 1990
  • Russell C. Eberhart
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 0 0 - 2
This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features:

Artificial Intelligence IV

  • 1st Edition
  • August 12, 1990
  • P. Jorrand + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 7 8 - 1
Presenting recent results and ongoing research in Artificial Intelligence, this book has a strong emphasis on fundamental questions in several key areas: programming languages, automated reasoning, natural language processing and computer vision.AI is at the source of major programming language design efforts. Different approaches are described, with some of their most significant results: languages combining logic and functional styles, logic and parallel, functional and parallel, logic with constraints.A central problem in AI is automated reasoning, and formal logic is, historically, at the root of research in this domain. This book presents results in automatic deduction, non-monotonic reasoning, non-standard logic, machine learning, and common-sense reasoning. Proposals for knowledge representation and knowledge engineering are described and the neural net challenger to classical symbolic AI is also defended.Finally, AI systems must be able to interact with their environment in a natural and autonomous way. Natural language processing is an important part of this. Various results are presented in discourse planning, natural language parsing, understanding and generation. The autonomy of a machine for perception of its physical environment is also an AI problem and some research in image processing and computer vision is described.

Machine Learning

  • 1st Edition
  • August 1, 1990
  • Yves Kodratoff + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 0 5 5 - 2
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

Minimalist Mobile Robotics

  • 1st Edition
  • July 28, 1990
  • Jonathan H. Connell
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 1 7 1 - 9
Rather than using traditional artificial intelligence techniques, which are ineffective when applied to the complexities of real-world robot navigaiton, Connell describes a methodology of reconstructing intelligent robots with distributed, multiagent control systems. After presenting this methodology, hte author describes a complex, robust, and successful application-a mobile robot "can collection machine" which operates in an unmodified offifce environment occupied by moving people.

Decentralized A.I

  • 1st Edition
  • July 6, 1990
  • Y. Demazeau + 1 more
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 9 2 4 - 7
Much research in Artificial Intelligence deals with a single agent having complete control over the world. A variation of this is Distributed AI (DAI), which is concerned with the collaborative solution of global problems by a distributed group of entities. This book deals with Decentralized AI (DzAI), which is concerned with the activity of an autonomous agent in a multi-agent world. The word ``agent'' is used in a broad sense, to designate an intelligent entity acting rationally and intentionally with respect to its goals and the current state of its knowledge. A number of these agents coexist and may collaborate with other agents in a common world; each agent may accomplish its own tasks, or cooperate with other agents to perform a personal or global task. The agents have imperfect knowledge about each other and about their common world, which they can update either through perception of the world, or by communication with each other.The papers were originally presented at a workshop held at King's College, Cambridge, and have been revised for this book.

Computational Intelligence, II

  • 1st Edition
  • June 1, 1990
  • G. Mauri + 2 more
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 7 2 8 - 1
The focus of this volume is ``Heterogeneous Knowledge and Problem Solving Integration'', i.e. the combined use of different knowledge representation and problem solving paradigms.This is a central topic for the design and implementation of problem solving systems, since, from a pragmatic and engineering standpoint, the solution of a large class of problems cannot take place within one single representation language or problem solving paradigm. Heterogeneous systems represent not only a pragmatic answer, but also a theoretical alternative to the homogeneous paradigms.

Machine Learning Proceedings 1990

  • 1st Edition
  • June 1, 1990
  • Bruce Porter + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 8 5 8 - 0
Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin. The book focuses on the progress in the interest in machine learning, including methodologies, approaches, and techniques. The selection first offers information on knowledge acquisition from examples using maximal representation learning, performance analysis of a probabilistic inductive learning system, and a comparative study of ID3 and backpropagation for English text-to-speech mapping. The text then examines learning from data with bounded inconsistency, improving fit-and-split algorithms, and an incremental method for finding multivariate splits for decision trees. Topics include issues for decision-tree induction, learning and approximation, conceptual-set-covering algorithm, bounded inconsistency, implementation, and examples of incremental processes. The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning. Learning procedures by environment-driven constructive induction and improving the performance of genetic algorithms in automated discovery of parameters are also discussed. The selection is a valuable source of data for researchers interested in machine learning.

Readings in Speech Recognition

  • 1st Edition
  • May 1, 1990
  • Alexander Waibel + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 5 8 4 - 7
After more than two decades of research activity, speech recognition has begun to live up to its promise as a practical technology and interest in the field is growing dramatically. Readings in Speech Recognition provides a collection of seminal papers that have influenced or redirected the field and that illustrate the central insights that have emerged over the years. The editors provide an introduction to the field, its concerns and research problems. Subsequent chapters are devoted to the main schools of thought and design philosophies that have motivated different approaches to speech recognition system design. Each chapter includes an introduction to the papers that highlights the major insights or needs that have motivated an approach to a problem and describes the commonalities and differences of that approach to others in the book.

Handbook of Human-Computer Interaction

  • 1st Edition
  • March 28, 1990
  • M.G. Helander
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 1 3 - 8
This Handbook is concerned with principles of human factors engineering for design of the human-computer interface. It has both academic and practical purposes; it summarizes the research and provides recommendations for how the information can be used by designers of computer systems. The articles are written primarily for the professional from another discipline who is seeking an understanding of human-computer interaction, and secondarily as a reference book for the professional in the area, and should particularly serve the following: computer scientists, human factors engineers, designers and design engineers, cognitive scientists and experimental psychologists, systems engineers, managers and executives working with systems development.The work consists of 52 chapters by 73 authors and is organized into seven sections. In the first section, the cognitive and information-processing aspects of HCI are summarized. The following group of papers deals with design principles for software and hardware. The third section is devoted to differences in performance between different users, and computer-aided training and principles for design of effective manuals. The next part presents important applications: text editors and systems for information retrieval, as well as issues in computer-aided engineering, drawing and design, and robotics. The fifth section introduces methods for designing the user interface. The following section examines those issues in the AI field that are currently of greatest interest to designers and human factors specialists, including such problems as natural language interface and methods for knowledge acquisition. The last section includes social aspects in computer usage, the impact on work organizations and work at home.

Languages, Compilers and Run-time Environments for Distributed Memory Machines

  • 1st Edition
  • Volume 3
  • March 15, 1990
  • J. Saltz + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 3 8 - 1
Papers presented within this volume cover a wide range of topics related to programming distributed memory machines. Distributed memory architectures, although having the potential to supply the very high levels of performance required to support future computing needs, present awkward programming problems. The major issue is to design methods which enable compilers to generate efficient distributed memory programs from relatively machine independent program specifications. This book is the compilation of papers describing a wide range of research efforts aimed at easing the task of programming distributed memory machines.

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