A reliability model for real-time rule-based expert systems

This paper uses two modeling tools to analyze the reliability of real-time expert systems: (1) a stochastic Petri net (SPN) for computing the conditional response time distribution given that a fixed number of expert system match-select-act cycles are executed, and (2) a simulation search tree for computing the distribution of expert system match-select-act cycles for formulating a control strategy in response to external events. By modeling the intrinsic match-select-act cycle of expert systems and associating rewards rates with markings of the SPN, the response time distribution for the expert system to reach a decision can be computed as a function of design parameters, thereby facilitating the assessment of reliability of expert systems in the presence of real-time constraints. The utility of the reliability model is illustrated with an expert system characterized by a set of design conditions under a real-time constraint. This reliability model allows the system designers to: (1) experiment with a range of selected parameter values; and (2) observe their effects on system reliability