Abstract
A formal tool, the High Level Fuzzy Petri Net, is proposed for representing and processing fuzzy production rules in a knowledge base. The basic net structures to model inference patterns in approximate reasoning are introduced. The chaining mechanism used and the modeling of rules with fuzzy quantifiers and certainty factors are discussed. We have also investigated the representation of parallel and conflicting rules. Two types of fuzzy reasoning algorithms, to answer data driven and goal driven queries are described. The issue of time complexity of the algorithms is also addressed.