Soar/PSM-E: investigating match parallelism in a learning production sytsem

Authors:
Milind Tambe;Dirk Kalp;Anoop Gupta;Charles Forgy; Brian Milnes;Allen Newell
Affiliations:
Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pa;Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pa;Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pa;Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pa;Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pa
Venue:
PPEALS '88 Proceedings of the ACM/SIGPLAN conference on Parallel programming: experience with applications, languages and systems
Year:
1988

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Abstract

Soar is an attempt to realize a set of hypotheses on the nature of general intelligence within a single system. Soar uses a production system (rule based system) to encode its knowledge base. Its learning mechanism, chunking, adds productions continuously to the production system. The process of searching for relevant knowledge, matching, is known to be a performance bottleneck in production systems. PSM-E is a C-based implementation of the OPS5 production system on the Encore Multimax that has achieved significant speedups in matching. In this paper we describe our implementation, Soar/PSM-E, of Soar on the Encore Multimax that is built on top of PSM-E. We first describe the extensions and modifications required to PSM-E in order to support Soar, especially the capability of adding productions at run time as required by chunking. We present the speedups obtained on Soar/PSM-E and discuss some effects of chunking on parallelism. We also analyze the performance of the system and identify the bottlenecks limiting parallelism. Finally, we discuss the work in progress to deal with some of them.