Symbolic Controller Synthesis for B\"uchi Specifications on Stochastic Systems

TL;DR

This paper develops an abstraction-based symbolic method for synthesizing policies that satisfy B"uchi specifications with high probability on continuous-state controlled Markov processes, addressing the challenge of approximating qualitative winning sets.

Contribution

It introduces a novel abstraction technique combining over- and under-approximations to approximate qualitative winning sets for B"uchi specifications on stochastic systems.

Findings

The method computes bounds on winning sets using nested fixed points in the $alculus.

The approach is demonstrated on two case studies showing practical applicability.

Abstract

We consider the policy synthesis problem for continuous-state controlled Markov processes evolving in discrete time, when the specification is given as a B\"uchi condition (visit a set of states infinitely often). We decompose computation of the maximal probability of satisfying the B\"uchi condition into two steps. The first step is to compute the maximal qualitative winning set, from where the B\"uchi condition can be enforced with probability one. The second step is to find the maximal probability of reaching the already computed qualitative winning set. In contrast with finite-state models, we show that such a computation only gives a lower bound on the maximal probability where the gap can be non-zero. In this paper we focus on approximating the qualitative winning set, while pointing out that the existing approaches for unbounded reachability computation can solve the second step. We provide an abstraction-based technique to approximate the qualitative winning set by simultaneously using an over- and under-approximation of the probabilistic transition relation. Since we are interested in qualitative properties, the abstraction is non-probabilistic; instead, the probabilistic transitions are assumed to be under the control of a (fair) adversary. Thus, we reduce the original policy synthesis problem to a B\"uchi game under a fairness assumption and characterize upper and lower bounds on winning sets as nested fixed point expressions in the $\mu$-calculus. This characterization immediately provides a symbolic algorithm scheme. Further, a winning strategy computed on the abstract game can be refined to a policy on the controlled Markov process. We describe a concrete abstraction procedure and demonstrate our algorithm on two case studies.