On the relationship between finite state machine and causal network representations for discrete event system modeling: initial results

Abstract

Shows the relationship between two discrete event system representations, finite state machines and causal networks. Finite state machine models have been used extensively for the supervisory control of logical (and timed, with some extension) discrete event systems. On the other hand, causal networks have been applied mainly to the diagnosis of discrete event systems. Advances in finite-state-machine based diagnosis and causal-network-based control have prompted an interest in understanding the relationship between these two representations. We describe initial findings concerning the mappings between these two representations for modeling synchronous system components, and discuss the implications of their relationships. We demonstrate the relationship using an example of a factory conveyor system.