Papers presented at the workshop are representative of the state-of-the art of artificial intelligence in real-time control. The issues covered included the use of AI methods in the design, implementation, testing, maintenance and operation of real-time control systems. While the focus was on the fundamental aspects of the methodologies and technologies, there were some applications papers which helped to put emerging theories into perspective. The four main subjects were architectural issues; knowledge - acquisition and learning; techniques; and scheduling, monitoring and management.
Section headings and selected papers: Keynote Addresses. Knowledge-based
vision systems in real-time control, M G Rodd & Q M Wu. D;stributed
estimation, inferencing and multi-sensor data fusion for real-time
supervisory control, C J Harris. Architectural Issues. Distributed
intelligent objects in an architecture for real-time monitoring and control,
V Lun & I M Macle;d. Knowledge-Acquisition and Learning. A clustering method
of knowledge acquisition in a real-time control system, Yu-Ji Huang.
Techniques. An extended feedback structure of intelligent computer-aided
control systems design based on object-oriented language, O Ono. An expert
self-learning fuzzy controller, Yu-Lan Zhou & Zhengyue ;iu. Scheduling,
Monitoring and Management. Neural network based real-time production
scheduling for industrial processes, Li-Wei Bao & Yong-Zai ;u. Application
Studies. Application of expert fuzzy controller in the penicillin
fermentation processes, E-Hui Xu et al . Author index. Keyword index.
