Machine Learning of Robot Assembly Plans

  • Alberto Maria Segre

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Alberto Maria Segre
    Pages 1-6
  3. Alberto Maria Segre
    Pages 7-33
  4. Alberto Maria Segre
    Pages 35-46
  5. Alberto Maria Segre
    Pages 47-60
  6. Alberto Maria Segre
    Pages 61-87
  7. Alberto Maria Segre
    Pages 89-148
  8. Alberto Maria Segre
    Pages 149-160
  9. Alberto Maria Segre
    Pages 161-173
  10. Back Matter
    Pages 175-233

About this book

Introduction

The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory.

Keywords

artificial intelligence classification intelligence knowledge representation learning linguistics machine learning modeling optimization programming robot robotics semantics uncertainty verification

Authors and affiliations

  • Alberto Maria Segre
    • 1
  1. 1.Cornell UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1691-6
  • Copyright Information Springer-Verlag US 1988
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8954-8
  • Online ISBN 978-1-4613-1691-6
  • Series Print ISSN 0893-3405
  • About this book
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