Data-driven parallel production systems

Much effort has been expended on developing special architectures dedicated to the efficient execution of production systems. While data-flow principles of execution offer the promise of high programmability for numerical computations, it is shown that the data-driven principles can also be applied...

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Bibliographic Details
Published in:IEEE transactions on software engineering Vol. 16; no. 3; pp. 281 - 293
Main Authors: Gaudiot, J.-L., Sohn, A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.1990
Institute of Electrical and Electronics Engineers
IEEE Computer Society
Subjects:
Algorithms
Applications
Applied sciences
Architecture
Artificial intelligence
Computer architecture
Computer programming
Computer science; control theory; systems
Conflict resolution
Data structures
Decision making
Delay
Exact sciences and technology
Expert systems
Learning and adaptive systems
Parallel processing
Pattern matching
Production systems
Software engineering
Multiprocessor
Production system
Expert system
Parallel processing
Artificial intelligence
Implementation
Data flow
ISSN:0098-5589, 1939-3520
Online Access:Get full text
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Summary:Much effort has been expended on developing special architectures dedicated to the efficient execution of production systems. While data-flow principles of execution offer the promise of high programmability for numerical computations, it is shown that the data-driven principles can also be applied to symbolic computations. In particular, a mapping of the RETE match algorithm along the line of production systems is considered. Bottlenecks of the RETE match algorithm in a multiprocessor environment are identified and possible solutions are suggested. The modifications to the actor set as well as the program graph design are shown for execution on the tagged data-flow computer. The results of a deterministic simulation of this multiprocessor architecture demonstrate that artificial intelligence production systems can be efficiently mapped on data-driven architectures.< >
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ISSN:0098-5589
1939-3520
DOI:10.1109/32.48936