integrating Marker Passing and Problem Solving
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A01=James A. Hendler
activation
AI decision making systems
Argum Ent
Author_James A. Hendler
Backward Chaining Rules
Category=JMR
Category=UYQ
Comm Ents
computational cognitive science
Connectionist Models
ents
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
evaluator
Examine Context Effects
Ext Ta Sk
heuristic search strategies
hockey
Hockey Pucks
Integrating Marker Passing
ISA Link
knowledge representation
Local Connectionist Models
Marker Passing System
massively
neural computation models
parallel
parallel activation planning algorithms
path
Path Evaluator
Path Strength
Plan Evaluator
pucks
Quillian's Model
Quillian’s Model
Sem Antics
Short SOA
Show Activation Effects
spreading
Spreading Activation
Spreading Activation Approach
Spreading Activation Mechanism
statem
Stim Ulus
symbolic reasoning
Ta Te
Tem Poral
Traditional Ai
Product details
- ISBN 9780898599824
- Weight: 680g
- Dimensions: 152 x 229mm
- Publication Date: 01 Nov 1987
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
James A. Hendler Department of Computer Science, The University of Maryland
