A pseudometric in supervisory control of probabilistic discrete event systems

The focus of this paper is the pseudometric used as a key concept in our previous work on optimal supervisory control of probabilistic discrete event systems. The pseudometric is employed to measure the behavioural similarity between probabilistic systems, and initially was defined as a greatest fixed point of a monotone function. This paper further characterizes the pseudometric. First, it gives a logical characterization of the pseudometric so that the distance between two systems is measured by a formula that distinguishes between the systems the most. A trace characterization of the pseudometric is then derived from the logical characterization, characterizing the similarity between systems from a language perspective. Further, the solution of the problem of approximation of a given probabilistic generator with another generator of a prespecified structure is suggested such that the new model is as close as possible to the original one in the pseudometric. The significance of the approximation is then discussed, especially with respect to previous work on optimal supervisory control of probabilistic discrete event systems.