Integrating Marker Passing and Problem

A recent area of interest in the Artificial Intelligence community has been the application of massively parallel algorithms to enhance the choice mechanism in traditional AI problems. This volume provides a detailed description of how marker-passing -- a parallel, non-deductive, spreading activation algorithm -- is a powerful approach to refining the choice mechanisms in an AI problem-solving system. The author scrutinizes the design of both the algorithm and the system, and then reviews the current literature and research in planning and marker passing. Also included: a comparison of this computer model with some standard cognitive models, and a comparison of this model to the "connectionist" approach.

Preface	p. vii
Acknowledgements	p. ix
Foundations	p. 1
Introduction	p. 7
on Problem-Solving and Planning	p. 37
on Marker-Passing and Spreading Activation	p. 57
Integrating Marker-Passing and Problem-Solving	p. 73
Inside the Path Evaluator	p. 110
Notes	p. 154
C-Scraps -- Adding Concurrency	p. 159
Cognitive Aspects	p. 181
Conclusion	p. 204
On Marker-Passing and Connectionism	p. 205
Scraps in Action	p. 228
Conclusion	p. 264
Bilbliography	p. 285
Author Index	p. 297
Subject Index	p. 301
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Integrating Marker Passing and Problem Solving : A Spreading Activation Approach to Improved Choice in Planning

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