Engineering constraint solvers for automatic analysis of probabilistic hybrid automata

In this article, we recall different approaches to the constraint-based, symbolic analysis of hybrid discrete-continuous systems and combine them to a technology able to address hybrid systems exhibiting both non-deterministic and probabilistic behavior akin to infinite-state Markov decision processes. To enable mechanized analysis of such systems, we extend the reasoning power of arithmetic satisfiability-modulo-theories (SMT) solving by, first, reasoning over ordinary differential equations (ODEs) and, second, a comprehensive treatment of randomized (also known as stochastic) quantification over discrete variables as well as existential quantification over both discrete and continuous variables within the mixed Boolean-arithmetic constraint system. This provides the technological basis for a constraint-based analysis of dense-time probabilistic hybrid automata, extending previous results addressing discrete-time automata (1). Generalizing SMT-based bounded model-checking of hybrid automata (2; 3), stochastic SMT including ODEs permits the direct analysis of probabilistic bounded reachability problems of dense-time probabilistic hybrid automata without resorting to approximation by intermediate finite-state abstractions.