Interactive, distributed, and embedded systems often behave stochastically, for example,
when inputs, message delays, or failures conform to a probability distribution. However …

This tutorial paper surveys the main features of Uppaal SMC, a model checking approach in
Uppaal family that allows us to reason on networks of complex real-timed systems with a …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

We present a general framework for applying machine-learning algorithms to the verification
of Markov decision processes (MDPs). The primary goal of these techniques is to improve …

We consider synthesis of control policies that maximize the probability of satisfying given
temporal logic specifications in unknown, stochastic environments. We model the interaction …

Actions description languages (ADLs), such as STRIPS, PDDL, and RDDL specify the input
format for planning algorithms. Unfortunately, their syntax is familiar to planning experts only …

We address the problem of model checking stochastic systems, ie, checking whether a
stochastic system satisfies a certain temporal property with a probability greater (or smaller) …

We consider the problem of computing the satisfaction probability of a formula for stochastic
models with parametric uncertainty. We show that this satisfaction probability is a smooth …

The ever-increasing deployment of autonomous Cyber-Physical Systems (CPSs)(eg,
autonomous cars, UAV) exacerbates the need for efficient formal verification methods. In this …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of control problems. However, applications in safety-critical …