Verification of qualitative properties of rule-based expert systems

Frequently expert systems are developed to operate in dynamic environments where they must reason about time-varying information and generate hypotheses, conclusions, and process in puts that can drastically influence the environment within which they operate. For instance, expert systems used for fault diagnosis and fault accommodation in nuclear power plants reason over sensor data and operator inputs, form fault hypotheses, make recommendations pertaining to safe process operation, and in crisis situations could generate command inputs to the process to help maintain safe operation. Clearly, there is a pressing need to verify and certify, that such expert systems are dependable in their operation and can reliably maintain adequate performance levels. In this article we develop a mathematical approach to verifying qualitative properties of rule-based expert systems that operate in dynamic and uncertain environments. First, we provide mathematical models for the expert system (including the knowledge-base and inference engine) and for the mechanisms for interfacing to the user inputs and the dynamic process. Next, using these mathematical models, we show that while the structure and interconnection of information in the knowledge base influence the expert system's ability to react appropriately in a dynamic environment, the qualitative properties of the full knowledge-base/inference engine loop must be considered to fully characterize an expert system's dynamic behavior: To illustrate the verification approach, we show how to model and analyze the qualitative properties of rule-based expert systems that solve a water-jug filling problem and a simple process control problem. Finally, in our concluding remarks, we highlight some limitations of our approach and provide some future directions for research.