A knowledge representation method for modeling rule-based systems

Knowledge representation has been the critical and intractable issues for a knowledge-based system, especially for complex or large systems. This paper proposes a knowledge representation approach for modeling rule-based systems using the defined fuzzy colored Petri nets (FCPN). The main advantages of this approach differ from the conventional ones consist in modeling rule-based systems particularly, realizing smaller model spaces, more compact data structures and fuzzy information processing. For a large or complex rule-based system, the advantages are more evident. An instance demonstrates that the presented approach is feasible and practical.