Petri net representation of fuzzy reasoning under incomplete information

In this paper we present an algorithm that performs reasoning processes on fuzzy knowledge bases with chaining between rules. The algorithm we propose permits obtaining valid inferences even in situations where part of the variables in the knowledge base are unknown. The support for the description of the execution algorithm is provided by the Petri net formalism, which organizes in a convenient way all the information in the base, and expresses in a simple way this and other processes performed onto it. This simplicity is mainly achieved also because the execution of the fuzzy knowledge base is carried out in a parameterized truth space using the linguistic truth values defined by J.F. Baldwin (1979), which reduces the computational cost of the process and makes easier its mapping onto the Petri net formalism.