This research focuses on developing the scientific foundation and implementing practical tools for optimizing and synthesizing rule-based expert systems to meet specified response time constraints. Real-time rule-based expert systems are embedded artificial intelligence systems increasingly used in safety-critical applications such as airplane avionics, medical monitoring instruments, smart robots, and space vehicles. In addition to functional correctness, these systems must also satisfy stringent timing constraints. The result of missing a deadline in these systems may be catastrophic. If a given rule-based system cannot deliver an adequate performance in bounded time, then it has to be optimized or resynthesized. The first part of the project investigates several approaches (state-space-based and semantics-based) for optimizing the rule base of expert systems. The second part of the project investigates the optimization of the match phase, which has a highly unpredictable runtime. The development of a systematic methodology to tackle the optimization problem opens up new avenues to further enhance the runtime performance of rule-based systems in time-critical environments. This technology will play a key role in tomorrow's complex information and computing systems such as multimedia tools, virtual reality systems, smart robots, and high-bandwidth networks, where an intelligent interface is essential and time becomes an increasingly critical resource.
