Parallel artificial-intelligence search techniques for real-time applications. Master's thesis
State-space search is an important component of many problem-solving methodologies. The computational models within Artificial Intelligence depend heavily upon state-space 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.
- Research Organization:
- Air Force Inst. of Tech., Wright-Patterson AFB, OH (USA). School of Engineering
- OSTI ID:
- 6984920
- Report Number(s):
- AD-A-190683/3/XAB; AFIT/GCS/ENG-87D-24
- Country of Publication:
- United States
- Language:
- English
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