In: International Journal of Intelligent Systems, pages 715-742. John Wiley & Sons, 1992.
Abstract: Many different approaches, mainly based on logical formalisms, have been proposed for modeling causal knowledge and the inferential mechanisms based on this type of knowledge. In this article we present an alternative approach to this problem in which the semantics of a causal model is provided by adopting Petri Nets. We show how this scheme of modeling is powerful enough to capture all crucial aspects of the corresponding causal model, without resorting to very complex structures; indeed, the model is obtained using a particular type of deterministic Petri net. Moreover, a complete formalization of the aspects concerning the correctness of the represented causal model is provided in terms of reachability in the Petri net. We believe that this aspect is very important in the knowledge acquisition phase when precise correctness criteria should be defined and respected in the construction of the model. We analyze some of these criteria and we discuss an algorithm (based on a backward simulation of the net) capable of discovering incorrectness by exploiting analysis tools available for Petri nets and the explicit parallelism of the model