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We present a scalable methodology to verify stochastic hybrid systems for inequality linear temporal logic (iLTL) or inequality metric interval temporal logic (iMITL). Using the Mori–Zwanzig reduction method, we construct a finite-state Markov chain reduction of a given stochastic hybrid system and prove that this reduced Markov chain is approximately equivalent to the original system in a distributional sense. Approximate equivalence of the stochastic hybrid system and its Markov chain reduction means that analyzing the Markov chain with respect to a suitably strengthened property allows us to conclude whether the original stochastic hybrid system meets its temporal logic specifications. Based on this, we propose the first statistical model checking algorithms to verify stochastic hybrid systems against correctness properties, expressed in iLTL or iMITL. The scalability of the proposed algorithms is demonstrated by a case study. [ABSTRACT FROM AUTHOR]

Model checking systems formalized using probabilistic models such as discrete time Markov chains (DTMCs) and Markov decision processes (MDPs) can be reduced to computing constrained reachability properties. Linear programming methods to compute reachability probabilities for DTMCs and MDPs do not scale to large models. Thus, model checking tools often employ iterative methods to approximate reachability probabilities. These approximations can be far from the actual probabilities, leading to inaccurate model checking results. On the other hand, specialized techniques employed in existing state-of-the-art exact quantitative model checkers, don't scale as well as their iterative counterparts. In this work, we present a new model checking algorithm that improves the approximate results obtained by scalable iterative techniques to compute exact reachability probabilities. Our techniques are implemented as an extension of the PRISM model checker and are evaluated against other exact quantitative model checking engines. [ABSTRACT FROM AUTHOR]

In this article, we introduce the model of finite state probabilistic monitors (FPM), which are finite state automata on infinite strings that have probabilistic transitions and an absorbing reject state. FPMs are a natural automata model that can be seen as either randomized run-time monitoring algorithms or as models of open, probabilistic reactive systems that can fail. We give a number of results that characterize, topologically as well as with respect to their computational power, the sets of languages recognized by FPMs. We also study the emptiness and universality problems for such automata and give exact complexity bounds for these problems. [ABSTRACT FROM AUTHOR]

In this work, we study the problem of statistically verifying Probabilistic Computation Tree Logic (PCTL) formulas on discrete-time Markov chains (DTMCs) with stratified and antithetic samples. We show that by properly choosing the representation of the DTMCs, semantically negatively correlated samples can be generated for a fraction of PCTL formulas via the stratified or antithetic sampling techniques. Using stratified or antithetic samples, we propose statistical verification algorithms with asymptotic correctness guarantees based on sequential probability ratio tests, and show that these algorithms are more sample-efficient than the algorithms using independent Monte Carlo sampling. Finally, the efficiency of the statistical verification algorithm with stratified and antithetic samples is demonstrated by numerical experiments on several benchmarks. [ABSTRACT FROM AUTHOR]

Statistical model checking of unbounded time properties is challenging, because it requires an algorithm to estimate the measure of paths satisfying an unbounded until property from samples of finite length paths. In this paper, we survey all proposed algorithms for this problem, and critically evaluate them. [ABSTRACT FROM AUTHOR]

Statistical model checking (SMC) is a powerful and widely used approach that consists in estimating the probability for a system to satisfy a temporal property. This is done by monitoring a finite number of executions of the system, and then extrapolating the result using statistics. The answer is correct up to some confidence that can be parameterized by the user. It is known that SMC mitigates the state-space explosion problem and allows us to handle requirements that cannot be expressed in classical temporal logics. The approach has been implemented in several toolsets, and successfully applied in a wide range of diverse areas such as systems biology, robotic, or automotive. The objectives of this special issue are (1) to survey existing results on SMC, (2) to propose SMC algorithms for a larger class of systems, and (3) to show the applicability of SMC to new emerging applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a counterexample guided abstraction refinement (CEGAR) framework for systems modelled as rectangular hybrid automata. The main difference, between our approach and previous proposals for CEGAR for hybrid automata, is that we consider the abstractions to be hybrid automata as well, as opposed to finite state systems. We show that the CEGAR scheme is semi-complete for the class of rectangular hybrid automata and complete for the subclass of initialized rectangular automata. We have implemented the CEGAR based algorithm in a tool called Hare, that makes calls to HyTech to analyze the abstract models and validate the counterexamples. The experimental evaluations demonstrate the merits of the approach. [ABSTRACT FROM AUTHOR]

In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic, it can be deployed at scale and integrated into existing massive open online courses. [ABSTRACT FROM AUTHOR]