Incremental Control Synthesis in Probabilistic Environments with Temporal Logic Constraints
This work addresses control synthesis in probabilistic environments for applications like robotics or autonomous systems, but it is incremental as it builds on existing methods by improving efficiency.
The paper tackles the problem of optimal control synthesis for a deterministic plant interacting with probabilistic agents in a graph-like environment, aiming to maximize the probability of satisfying a temporal logic formula, and demonstrates that their incremental approach reduces computation time and memory usage compared to a classical non-incremental method.
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.