Parallel expert system search techniques for a real-time application

Abstract

Expert systems are being used to govern the intelligent control of the Robotic Air Vehicle (RAV) which is currently a research project at the Air Force Avionics Laboratory. Due to the nature of the RAV system the associated expert system needs to perform in a demanding real-time environment. The use of a parallel processing capability to support the associated computational requirement may be critical in this application. Thus, parallel search algorithms for real-time expert systems are designed, analyzed and synthesized on the Texas Instruments (TI) Explorer and Intel Hypercube. Examined is the process involved with transporting the RAV expert systems from the TI Explorer, where they are implemented in the Automated Reasoning Tool (ART), to the iPSC Hypercube, where the system is synthesized using Concurrent Common LISP. The performance characteristics of the parallel implementation of these expert systems on the iPSC Hypercube are compared to the TI Explorer implementation.