tailieunhanh - Thời gian thực - hệ thống P12

OPTIMIZATION OF RULE-BASED SYSTEMS As we have seen in chapters 10 and 11, embedded rule-based expert systems must satisfy stringent timing constraints when applied to real-time environments. We now describe a novel approach to reduce the response time of rule-based expert systems. This optimization is needed when a rule-based system does not meet the specified response-time constraints. Our optimization method is based on a construction of the reduced cycle-free finite-state space-graph. | Real-Time Systems Scheduling Analysis and Verification. Albert M. K. Cheng Copyright 2002 John Wiley Sons Inc. ISBN 0-471-18406-3 CHAPTER 12 OPTIMIZATION OF RULE-BASED SYSTEMS As we have seen in chapters 10 and 11 embedded rule-based expert systems must satisfy stringent timing constraints when applied to real-time environments. We now describe a novel approach to reduce the response time of rule-based expert systems. This optimization is needed when a rule-based system does not meet the specified response-time constraints. Our optimization method is based on a construction of the reduced cycle-free finite-state space-graph. In contrast with traditional state-space graph derivation algorithms our optimization algorithm starts from the final states fixed points and gradually expands the state-space graph until all of the states with a reachable fixed point are found. The new and optimized rule-based system is then synthesized from the constructed state-space graph. We present several algorithms implementing the optimization method. They vary in complexity as well as in the usage of concurrency and state-equivalence both targeting to minimize the size of the optimized state-space graph. The optimized rule based systems generally 1 have better response time that is require fewer number of rule firings to reach the fixed point 2 are stable that is have no cycles that would result in the instability of execution and 3 include no redundant rules. The actual results of the optimization depend on the algorithm used. We also address the issue of deterministic execution and propose optimization algorithms that generate the rule bases with a single corresponding fixed point for every initial state. The synthesis method also determines a tight response-time bound of the new system and can identify unstable states in the original rule base. No information other than the rule-based real-time decision program itself is given to the optimization method. The optimized system is .