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Connectionistic Problem Solving

Computational Aspects of Biological Learning

  • Steven E. Hampson

Table of contents

  1. Front Matter
    Pages i-ix
  2. Steven E. Hampson
    Pages 1-16
  3. Steven E. Hampson
    Pages 17-39
  4. Steven E. Hampson
    Pages 41-51
  5. Steven E. Hampson
    Pages 53-69
  6. Steven E. Hampson
    Pages 71-96
  7. Steven E. Hampson
    Pages 97-113
  8. Steven E. Hampson
    Pages 115-137
  9. Steven E. Hampson
    Pages 139-166
  10. Steven E. Hampson
    Pages 167-181
  11. Steven E. Hampson
    Pages 183-192
  12. Steven E. Hampson
    Pages 193-198
  13. Steven E. Hampson
    Pages 199-204
  14. Steven E. Hampson
    Pages 205-258
  15. Back Matter
    Pages 259-276

About this book

Introduction

1. 1 The problem and the approach The model developed here, which is actually more a collection of com­ ponents than a single monolithic structure, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initial chapters develop the basic components of the sys­ tem at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more con­ cerned with the problems of assembling sequences of actions in order to achieve a given goal state. The model is referred to as connectionistic rather than neural, be­ cause, while the basic components are neuron-like, there is only limited commitment to physiological realism. Consequently the neuron-like ele­ ments are referred to as "nodes" rather than "neurons". The model is directed more at the behavioral level, and at that level, numerous con­ cepts from animal learning theory are directly applicable to connectionis­ tic modeling. An attempt to actually implement these behavioral theories in a computer simulation can be quite informative, as most are only partially specified, and the gaps may be apparent only when actual­ ly building a functioning system. In addition, a computer implementa­ tion provides an improved capability to explore the strengths and limita­ tions of the different approaches as well as their various interactions.

Keywords

Extension artificial intelligence behavior intelligence learning memory modeling problem solving proving simulation

Authors and affiliations

  • Steven E. Hampson
    • 1
  1. 1.Department of Information & Computer ScienceUniversity of CaliforniaIrvineUSA

Bibliographic information