  In this paper, we present a method for optimal control synthesis of a plant
that interacts with a set of agents in a graph-like environment. The control
specification is given as a temporal logic statement about some properties that
hold at the vertices of the environment. The plant is assumed to be
deterministic, while the agents are probabilistic Markov models. The goal is to
control the plant such that the probability of satisfying a syntactically
co-safe Linear Temporal Logic formula is maximized. We propose a
computationally efficient incremental approach based on the fact that temporal
logic verification is computationally cheaper than synthesis. We present a
case-study where we compare our approach to the classical non-incremental
approach in terms of computation time and memory usage.
