The design of a marker passing architecture for knowledge processing

Authors:
Wing Lee;Dan Moldovan
Affiliations:
Department of Electrical Engineering -Systems, University of Southern California, Los Angeles, California;Department of Electrical Engineering -Systems, University of Southern California, Los Angeles, California
Venue:
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Year:
1990

Citing 2
Cited 0

Integrating Marker Passing and Problem Solving: A Spreading Activation Approach to Improved Choice in Planning

Integrating Marker Passing and Problem Solving: A Spreading Activation Approach to Improved Choice in Planning
Classification in the KL-ONE knowledge representation system

IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1

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Abstract

Knowledge processing is very demanding on computer architectures. Knowledge processing generates subcomputation paths at an exponential rate. It is memory intensive and has high communication requirements. Marker passing architectures are good candidates to solve knowledge processing problems. In this paper, we justify the design decisions made for the Semantic Network Array Processor (SNAP). Important aspects of SNAP are: the instruction set, markers, relations, propagation rules, interconnection network, and granularity. These features are compared to those in NETL and the Connection Machine.