Parallel Artificial Intelligence Search Techniques for Real Time Applications.

摘要

Abstract : State space search is an important component of many problem solving methodologies. The computational models within Artificial Intelligence depend heavily upon state spaces searches. Production systems are one such computational model. Production systems are being explored for real-time environments where timing is of a critical nature. Parallel processing of these systems and in particular concurrent state space searching seems to provide a promising method to increase the performance (effective and efficient) of production systems in the real-time environment. Production systems in the form of expert systems, for example, are being used to govern the intelligent control of the Robotic Air Vehicle (RAV) which is currently a research project at the Air Force Wright Aeronautical Laboratories. 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. Keywords: Theses, Production, System control.