ANALYSIS OF REAL-TIME RULE-BASED SYSTEMS WITH BEHAVIORAL CONSTRAINT ASSERTIONS SPECIFIED IN ESTELLA

Rule-based expert systems are increasingly used to monitor and control the operations of complex real-time systems which require intensive k nowledge-decision processing and human expertise. These embedded AI sy stems must respond to events in the rapidly changing external environm ent so that the results of the expert system's computation in each mon itor-respond cycle are valid in safely operating the real-time system. Determining how fast an expert system can respond under all possible situations is a difficult problem. We have developed an efficient anal ysis methodology for a large class of rule-based EQL programs to deter mine whether a program in this class has bounded response time. In par ticular, we have identified several sets of primitive behavioral const raint assertions: an EQL program which satisfies all constraints in on e of these sets of assertions is guaranteed to have bounded response t ime. Here, we enhance the applicability of our analysis technique by i ntroducing a facility with which the rule-based programmer can specify application-specific knowledge that is too difficult to be mechanical ly detected in the new language Estella in order to determine the perf ormance of an even wider range of programs. We also describe efficient algorithms for implementing the analysis tools.