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<p>Rule-based expert systems are increasingly used to monitor and control the operations of complex real-time systems which require intensive knowledge-decision processing and human expertise. These embedded AI systems must respond to events in the rapidly changing external environment so that the results of the expert system's computation in each monitor-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 analysis methodology for a large class of rule-based EQL programs to determine whether a program in this class has bounded response time. In particular, we have identified several sets of primitive behavioral constraint assertions: an EQL program which satisfies all constraints in one of these sets of assertions is guaranteed to have bounded response time. Here, we enhance the applicability of our analysis technique by introducing a facility with which the rule-based programmer can specify application-specific knowledge that is too difficult to be mechanically detected in the new language Estella in order to determine the performance of an even wider range of programs. We also describe efficient algorithms for implementing the analysis tools.</p>
real-time rule-based systems; behavioral constraint assertions; Estella; rule-based expert systems; knowledge-decision processing; human expertise; monitor-respond cycle; bounded response time; rule-based programmer; application-specific knowledge; constraint handling; expert systems; formal specification; knowledge representation; real-time systems

A. Mok, A. Cheng, J. Browne and R. Wang, "Analysis of Real-Time Rule-Based Systems with Behavioral Constraint Assertions Specified in Estella," in IEEE Transactions on Software Engineering, vol. 19, no. , pp. 863-885, 1993.
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