A Model‐Based Approach to Investigate Performance Improvements in Rule‐Based Expert Systems
针对大型基于规则的专家系统性能效率问题，通过将规则库建模为网络并模拟，研究改变规则测试顺序、参数分解和前提子句重排序带来的性能提升。
ABSTRACT One of the major issues in the development of large, rule‐based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.