Inference Approach Based on Petri Nets

Abstract An inference approach is proposed by formulating reasoning processes as particular evolutions of Petri nets. It can be used to design an intelligent agent that executes tasks in a given environment. First, a symbol Petri net is defined to represent a Boolean variable describing a distinct aspect of an environment. Second, a propositional logic sentence in a conjunctive normal form, which may express some background knowledge or a sequence of percepts made by an agent, is formulated as a linear constraint, called as a semantic constraint. Third, an algorithm is constructed to design monitor places enforcing semantic constraints on symbol Petri nets, and its resultant net is called a knowledge Petri net representing relevant knowledge. Fourth, a reasoning algorithm is presented based on a newly defined transition-firing rule of the knowledge Petri net, and can be used to infer or reveal hidden facts. The proposed inference algorithm is efficient since its time computational complexity is proven to be polynomial with respect to the number of Boolean variables. The wumpus world problem is taken as an example to illustrate and verify it.