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.
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.

integrating Marker Passing and Problem Solving: A Spreading Activation Approach To Improved Choice in Planning
312
integrating Marker Passing and Problem Solving: A Spreading Activation Approach To Improved Choice in Planning
312Related collections and offers
Product Details
ISBN-13: | 9781317766605 |
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Publisher: | Taylor & Francis |
Publication date: | 05/12/2014 |
Series: | Artificial Intelligence Series |
Sold by: | Barnes & Noble |
Format: | eBook |
Pages: | 312 |
File size: | 39 MB |
Note: | This product may take a few minutes to download. |