Efficient Pattern Matching for Non-strongly Sequential Term Rewriting Systems

Abstract

Pattern matching is a fundamental feature in many applications such as rule-based expert systems. Usually, patterns are pre-processed into a deterministic finite automaton. With ambiguous patterns a subject term may be an instance of more than one pattern and so a priority rule is usually engaged to select the matched pattern. The pre-processing of the patterns adds new patterns, which are instances of the original ones. When the original patterns are ambiguous, some of the instances supplied may be irrelevant. Their introduction causes unnecessary increase of space requirements. Furthermore, they slow down the matching process. Here, we devise a new pre-processing operation that identifies and avoids including such irrelevant instances and hence improves space and time requirements for the matching automaton and process.