PrIC3: Property Directed Reachability for MDPs

IC3 has been a leap forward in symbolic model checking. This paper proposes PrIC3 (pronounced pricy-three), a conservative extension of IC3 to symbolic model checking of MDPs. Our main focus is to develop the theory underlying PrIC3. Alongside, we present a first implementation of PrIC3 including the key ingredients from IC3 such as generalization, repushing, and propagation

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Developing Learning Analytics for Epistemic Commitments in a Collaborative Information Seeking Environment

Learning analytics sits at the confluence of learning, information, and computer sciences. Using a distinctive account of learning analytics as a form of assessment, I first argue for its potential in pedagogically motivated learning design, suggesting a particular construct – epistemic cognition in literacy contexts – to probe using learning analytics. I argue for a recasting of epistemic cognition as ‘epistemic commitments’ in collaborative information tasks drawing a novel alignment between information seeking and multiple document processing (MDP) models, with empirical and theoretical grounding given for a focus on collaboration and dialogue in such activities. Thus, epistemic commitments are seen in the ways students seek, select, and integrate claims from multiple sources, and the ways in which their collaborative dialogue is brought to bear in this activity. Accordingly, the empirical element of the thesis develops two pedagogically grounded literacy based tasks: a MDP task, in which pre-selected documents were provided to students; and a collaborative information seeking task (CIS), in which students could search the web. These tasks were deployed at scale (n > 500) and involved writing an evaluative review, followed by a pedagogically supported peer assessment task. Assessment outcomes were analysed in the context of a new epistemic commitments-oriented set of trace data, and psychometric data regarding the participants’ epistemic cognition. Demonstrating the value of the methodological and conceptual approach taken, qualitative analyses indicate clear epistemic activity, and stark differences in behaviour between groups, the complexity of which is challenging to model computationally. Despite this complexity, quantitative analyses indicate that up to 30% of variance in output scores can be modelled using behavioural indicators. The explanatory potential of behaviourally-oriented models of epistemic commitments grounded in tool-interaction and collaborative dialogue is demonstrated. The thesis provides an exemplification of theoretically positioned analytic development, drawing on interdisciplinary literatures in addressing complex learning contexts

On the analysis of stochastic timed systems

The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions. We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Efficient Sensitivity Analysis for Parametric Robust Markov Chains

We provide a novel method for sensitivity analysis of parametric robust Markov chains. These models incorporate parameters and sets of probability distributions to alleviate the often unrealistic assumption that precise probabilities are available. We measure sensitivity in terms of partial derivatives with respect to the uncertain transition probabilities regarding measures such as the expected reward. As our main contribution, we present an efficient method to compute these partial derivatives. To scale our approach to models with thousands of parameters, we present an extension of this method that selects the subset of $k$ parameters with the highest partial derivative. Our methods are based on linear programming and differentiating these programs around a given value for the parameters. The experiments show the applicability of our approach on models with over a million states and thousands of parameters. Moreover, we embed the results within an iterative learning scheme that profits from having access to a dedicated sensitivity analysis

Efficient Sensitivity Analysis for Parametric Robust Markov Chains

We provide a novel method for sensitivity analysis of parametric robust Markov chains. These models incorporate parameters and sets of probability distributions to alleviate the often unrealistic assumption that precise probabilities are available. We measure sensitivity in terms of partial derivatives with respect to the uncertain transition probabilities regarding measures such as the expected reward. As our main contribution, we present an efficient method to compute these partial derivatives. To scale our approach to models with thousands of parameters, we present an extension of this method that selects the subset of $k$ parameters with the highest partial derivative. Our methods are based on linear programming and differentiating these programs around a given value for the parameters. The experiments show the applicability of our approach on models with over a million states and thousands of parameters. Moreover, we embed the results within an iterative learning scheme that profits from having access to a dedicated sensitivity analysis.Comment: To be presented at CAV 202