Robust Markov decision processes (r-MDPs) extend MDPs by explicitly modelling epistemic uncertainty about transition dynamics. Learning r-MDPs from interactions with an unknown environment enables the synthesis of robust policies with provable (PAC) guarantees on performance, but this can require a large number of sample interactions. We propose novel methods for solving and learning r-MDPs based on factored state-space representations that leverage the independence between model uncertainty across system components. Although policy synthesis for factored r-MDPs leads to hard, non-convex optimisation problems, we show how to reformulate these into tractable linear programs. Building on these, we also propose methods to learn factored model representations directly. Our experimental results show that exploiting factored structure can yield dimensional gains in sample efficiency, producing more effective robust policies with tighter performance guarantees than state-of-the-art methods.

Abstract We introduce a bisimulation learning algorithm for non-deterministic transition systems. We generalise bisimulation learning to systems with bounded branching and extend its applicability to model checking branching-time temporal logic, while previously it was limited to deterministic systems and model checking linear-time properties. Our method computes a finite stutter-insensitive bisimulation quotient of the system under analysis, represented as a decision tree. We adapt the proof rule for well-founded bisimulations to an iterative procedure that trains candidate decision trees from sample transitions of the system, and checks their validity over the entire transition relation using SMT solving. This results in a new technology for model checking CTL* without the next-time operator. Our technique is sound, entirely automated, and yields abstractions that are succinct and effective for formal verification and system diagnostics. We demonstrate the efficacy of our method on diverse benchmarks comprising concurrent software, communication protocols and robotic scenarios. Our method performs comparably to mature tools in the special case of LTL model checking, and outperforms the state of the art in CTL and CTL* model checking for systems with very large and countably infinite state space.

Abstract We present a data-driven approach for producing policies that are provably robust across unknown stochastic environments. Existing approaches can learn models of a single environment as an interval Markov decision processes (IMDP) and produce a robust policy with a probably approximately correct (PAC) guarantee on its performance. However these are unable to reason about the impact of environmental parameters underlying the uncertainty. We propose a framework based on parametric Markov decision processes with unknown distributions over parameters. We learn and analyse IMDPs for a set of unknown sample environments induced by parameters. The key challenge is then to produce meaningful performance guarantees that combine the two layers of uncertainty: (1) multiple environments induced by parameters with an unknown distribution; (2) unknown induced environments which are approximated by IMDPs. We present a novel approach based on scenario optimisation that yields a single PAC guarantee quantifying the risk level for which a specified performance level can be assured in unseen environments, plus a means to trade-off risk and performance. We implement and evaluate our framework using multiple robust policy generation methods on a range of benchmarks. We show that our approach produces tight bounds on a policy’s performance with high confidence.

Abstract We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space. Our novel technique computes stutter-insensitive bisimulations of deterministic systems, which we characterize as the problem of learning a state classifier together with a ranking function for each class. Our procedure learns a candidate state classifier and candidate ranking functions from a finite dataset of sample states; then, it checks whether these generalise to the entire state space using satisfiability modulo theory solving. Upon the affirmative answer, the procedure concludes that the classifier constitutes a valid stutter-insensitive bisimulation of the system. Upon a negative answer, the solver produces a counterexample state for which the classifier violates the claim, adds it to the dataset, and repeats learning and checking in a counterexample-guided inductive synthesis loop until a valid bisimulation is found. We demonstrate on a range of benchmarks from reactive verification and software model checking that our method yields faster verification results than alternative state-of-the-art tools in practice. Our method produces succinct abstractions that enable an effective verification of linear temporal logic without next operator, and are interpretable for system diagnostics.

Abstract This paper is about shipping runtime verification to the masses. It presents the crucial technology enabling everyday car owners to monitor the behaviour of their cars in-the-wild. Concretely, we present an Android app that deploys rtlola runtime monitors for the purpose of diagnosing automotive exhaust emissions. For this, it harvests the availability of cheap Bluetooth adapters to the On-Board-Diagnostics (obd) ports, which are ubiquitous in cars nowadays. The app is a central piece in a set of tools and services we have developed for black-box analysis of automotive vehicles. We detail its use in the context of real driving emission (rde) tests and report on sample runs that helped identify violations of the regulatory framework currently valid in the European Union.

Abstract This paper is about shipping runtime verification to the masses. It presents the crucial technology enabling everyday car owners to monitor the behaviour of their cars in-the-wild. Concretely, we present an Android app that deploys rtlola runtime monitors for the purpose of diagnosing automotive exhaust emissions. For this, it harvests the availability of cheap bluetooth adapters to the On-Board-Diagnostics (obd) ports, which are ubiquitous in cars nowadays. We detail its use in the context of Real Driving Emissions (rde) tests and report on sample runs that helped identify violations of the regulatory framework currently valid in the European Union.