Refinement sensitive formal semantics of state machines with persistent choice

Modeling languages usually support two kinds of nondeterminism, an external one for interactions of a system with its environment, and one that stems from under-specification as familiar in models of behavioral requirements. Both forms of nondeterminism are resolvable by composing a system with an environment model and by refining under-specified behavior (respectively). Modeling languages usually dont support nondeterminism that is persistent in that neither the composition with an environment nor refinements of under-specification will resolve it. Persistent nondeterminism is used, e.g., for modeling faulty systems. We present a formal semantics for UML state machines enriched with an operator persistent choice that models persistent nondeterminism. This semantics is based on abstract models - μ-automata with a novel refinement relation - and a sound three-valued satisfaction relation for properties expressed in the μ-calculus. © 2009 Elsevier B.V. All rights reserved

A Probabilistic Kleene Theorem

International audienceWe provide a Kleene Theorem for (Rabin) probabilistic automata over finite words. Probabilistic automata generalize deterministic finite automata and assign to a word an acceptance probability. We provide probabilistic expressions with probabilistic choice, guarded choice, concatenation, and a star operator. We prove that probabilistic expressions and probabilistic automata are expressively equivalent. Our result actually extends to two-way probabilistic automata with pebbles and corresponding expressions

Deciding Probabilistic Automata Weak Bisimulation in Polynomial Time

Deciding in an efficient way weak probabilistic bisimulation in the context of Probabilistic Automata is an open problem for about a decade. In this work we close this problem by proposing a procedure that checks in polynomial time the existence of a weak combined transition satisfying the step condition of the bisimulation. We also present several extensions of weak combined transitions, such as hyper-transitions and the new concepts of allowed weak combined and hyper-transitions and of equivalence matching, that turn out to be verifiable in polynomial time as well. These results set the ground for the development of more effective compositional analysis algorithms for probabilistic systems.Comment: Polished version with a more complete running example and typo fixe

Cost Preserving Bisimulations for Probabilistic Automata

Probabilistic automata constitute a versatile and elegant model for concurrent probabilistic systems. They are equipped with a compositional theory supporting abstraction, enabled by weak probabilistic bisimulation serving as the reference notion for summarising the effect of abstraction. This paper considers probabilistic automata augmented with costs. It extends the notions of weak transitions in probabilistic automata in such a way that the costs incurred along a weak transition are captured. This gives rise to cost-preserving and cost-bounding variations of weak probabilistic bisimilarity, for which we establish compositionality properties with respect to parallel composition. Furthermore, polynomial-time decision algorithms are proposed, that can be effectively used to compute reward-bounding abstractions of Markov decision processes in a compositional manner

Weight Annotation in Information Extraction

The framework of document spanners abstracts the task of information extraction from text as a function that maps every document (a string) into a relation over the document's spans (intervals identified by their start and end indices). For instance, the regular spanners are the closure under the Relational Algebra (RA) of the regular expressions with capture variables, and the expressive power of the regular spanners is precisely captured by the class of VSet-automata -- a restricted class of transducers that mark the endpoints of selected spans. In this work, we embark on the investigation of document spanners that can annotate extractions with auxiliary information such as confidence, support, and confidentiality measures. To this end, we adopt the abstraction of provenance semirings by Green et al., where tuples of a relation are annotated with the elements of a commutative semiring, and where the annotation propagates through the positive RA operators via the semiring operators. Hence, the proposed spanner extension, referred to as an annotator, maps every string into an annotated relation over the spans. As a specific instantiation, we explore weighted VSet-automata that, similarly to weighted automata and transducers, attach semiring elements to transitions. We investigate key aspects of expressiveness, such as the closure under the positive RA, and key aspects of computational complexity, such as the enumeration of annotated answers and their ranked enumeration in the case of ordered semirings. For a number of these problems, fundamental properties of the underlying semiring, such as positivity, are crucial for establishing tractability

Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism

Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming—graphical models include Bayesian networks and factor graphs. In this paper we develop a new model of mixed (nondeterministic/probabilistic) automata that subsumes both nondeterministic automata and graphical probabilistic models. Mixed Automata are equipped with parallel composition, simulation relation, and support message passing algorithms inherited from graphical probabilistic models. Segala’s Probabilistic Automatacan be mapped to Mixed Automata

Decision algorithms for probabilistic simulations

Probabilistic phenomena arise in embedded, distributed, networked, biological and security systems, and are accounted for by various probabilistic modeling formalisms based on labelled transition systems. Among the most popular ones are homogeneous discretetime and continuous-time Markov chains (DTMCs and CTMCs) and their extensions with nondeterminism, which we will consider in this thesis. Simulation relations admit comparing the behavior of two models and provide the principal ingredients to perform abstractions of the models while preserving interesting properties. Intuitively, one model simulates another model if it can imitate all of its moves. Simulation preorders are compositional, thus allowing hierarchical verification and decomposition of difficult verification tasks into several subproblems. Recently, variants of simulation relations, such as simulatability and polynomially accurate probabilistic simulations, have been introduced to prove soundness of security protocols. The focus of this thesis lies in decision algorithms for various simulation preorders of probabilistic systems. We propose efficient decision algorithms and provide also experimental comparisons of these algorithms.In einem breiten Spektrum von Systemen, etwa bei eingebetteten, verteilten, netzwerkbasierten und biologischen System sowie im Bereich Security, treten Phänomene auf, die sich sehr gut durch Probabilismus beschreiben lassen. Als Modellierungsformalismus dienen dabei verschiedene probabilistische Erweiterungen von Transitionssystemen. Zu den wohl populärsten Formalismen dieser Art zählen hier homogene Markovketten (Markov chains) mit diskreter Zeit und Markovketten mit kontinuierlicher Zeit, bzw. deren Erweiterungen mit Nichtdeterminismus. Genau diese Klasse von Modellen betrachten wir in dieser Dissertation. Simulationsrelationen erlauben es, das Verhalten zweier Modelle in Beziehung zu setzen und liefern den grundlegenden Baustein, um Abstraktionen so zu betreiben, daß interessante Eigenschaften erhalten bleiben. Intuitiv gesprochen simuliert ein Modell ein anderes, wenn es alle Zustandsübergänge des anderen imitieren kann. Derartige Simulationsordnungen sind kompositional, daher erlauben sie hierarchische Verifikation und Zerlegung von Verifikationsaufgaben in kleinere Unterprobleme. Kürzlich wurden Simulationsrelationen eingeführt, wie etwa Simulatability und Polynomiell Akkurate Probabilstische Simulationen, um Korrektheit von Sicherheitsprotokollen zu zeigen. Der Schwerpunkt dieser Dissertation liegt auf Entscheidungsalgorithmen für verschiedene Simulationsordnungen auf probabilistischen Systemen. Wir stellen neue, effiziente Entscheidungsalgorithmen vor und vergleichen diese in Experimenten mit existierenden Algorithmen

Decision algorithms for modelling, optimal control and verification of probabilistic systems

Markov Decision Processes (MDPs) constitute a mathematical framework for modelling systems featuring both probabilistic and nondeterministic behaviour. They are widely used to solve sequential decision making problems and applied successfully in operations research, arti?cial intelligence, and stochastic control theory, and have been extended conservatively to the model of probabilistic automata in the context of concurrent probabilistic systems. However, when modeling a physical system they suffer from several limitations. One of the most important is the inherent loss of precision that is introduced by measurement errors and discretization artifacts which necessarily happen due to incomplete knowledge about the system behavior. As a result, the true probability distribution for transitions is in most cases an uncertain value, determined by either external parameters or con?dence intervals. Interval Markov decision processes (IMDPs) generalize classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that re?ects the absence of precise knowledge concerning transition probabilities. In this dissertation, we focus on decision algorithms for modelling and performance evaluation of such probabilistic systems leveraging techniques from mathematical optimization. From a modelling viewpoint, we address probabilistic bisimulations to reduce the size of the system models while preserving the logical properties they satisfy. We also discuss the key ingredients to construct systems by composing them out of smaller components running in parallel. Furthermore, we introduce a novel stochastic model, Uncertain weighted Markov Decision Processes (UwMDPs), so as to capture quantities like preferences or priorities in a nondeterministic scenario with uncertainties. This model is close to the model of IMDPs but more convenient to work with in the context of bisimulation minimization. From a performance evaluation perspective, we consider the problem of multi-objective robust strategy synthesis for IMDPs, where the aim is to ?nd a robust strategy that guarantees the satisfaction of multiple properties at the same time in face of the transition probability uncertainty. In this respect, we discuss the computational complexity of the problem and present a value iteration-based decision algorithm to approximate the Pareto set of achievable optimal points. Moreover, we consider the problem of computing maximal/minimal reward-bounded reachability probabilities on UwMDPs, for which we present an ef?cient algorithm running in pseudo-polynomial time. We demonstrate the practical effectiveness of our proposed approaches by applying them to a collection of real-world case studies using several prototypical tools.Markov-Entscheidungsprozesse (MEPe) bilden den Rahmen für die Modellierung von Systemen, die sowohl stochastisches als auch nichtdeterministisches Verhalten beinhalten. Diese Modellklasse hat ein breites Anwendungsfeld in der Lösung sequentieller Entscheidungsprobleme und wird erfolgreich in der Operationsforschung, der künstlichen Intelligenz und in der stochastischen Kontrolltheorie eingesetzt. Im Bereich der nebenläu?gen probabilistischen Systeme wurde sie konservativ zu probabilistischen Automaten erweitert. Verwendet man MEPe jedoch zur Modellierung physikalischer Systeme so zeigt es sich, dass sie an einer Reihe von Einschränkungen leiden. Eines der schwerwiegendsten Probleme ist, dass das tatsächliche Verhalten des betrachteten Systems zumeist nicht vollständig bekannt ist. Durch Messfehler und Diskretisierungsartefakte ist ein Verlust an Genauigkeit unvermeidbar. Die tatsächlichen Übergangswahrscheinlichkeitsverteilungen des Systems sind daher in den meisten Fällen nicht exakt bekannt, sondern hängen von äußeren Faktoren ab oder können nur durch Kon?denzintervalle erfasst werden. Intervall Markov-Entscheidungsprozesse (IMEPe) verallgemeinern klassische MEPe dadurch, dass die möglichen Übergangswahrscheinlichkeitsverteilungen durch Intervalle ausgedrückt werden können. IMEPe sind daher ein mächtiges Modellierungswerkzeug für probabilistische Systeme mit unbestimmtem Verhalten, dass sich dadurch ergibt, dass das exakte Verhalten des realen Systems nicht bekannt ist. In dieser Doktorarbeit konzentrieren wir uns auf Entscheidungsverfahren für die Modellierung und die Auswertung der Eigenschaften solcher probabilistischer Systeme indem wir Methoden der mathematischen Optimierung einsetzen. Im Bereich der Modellierung betrachten wir probabilistische Bisimulation um die Größe des Systemmodells zu reduzieren während wir gleichzeitig die logischen Eigenschaften erhalten. Wir betrachten außerdem die Schlüsseltechniken um Modelle aus kleineren Komponenten, die parallel ablaufen, kompositionell zu generieren. Weiterhin führen wir eine neue Art von stochastischen Modellen ein, sogenannte Unsichere Gewichtete Markov-Entscheidungsprozesse (UgMEPe), um Eigenschaften wie Implementierungsentscheidungen und Benutzerprioritäten in einem nichtdeterministischen Szenario ausdrücken zu können. Dieses Modell ähnelt IMEPe, ist aber besser für die Minimierung bezüglich Bisimulation geeignet. Im Bereich der Auswertung von Modelleigenschaften betrachten wir das Problem, Strategien zu generieren, die in der Lage sind den Nichtdeterminismus so aufzulösen, dass mehrere gewünschte Eigenschaften gleichzeitig erfüllt werden können, wobei jede mögliche Auswahl von Wahrscheinlichkeitsverteilungen aus den Übergangsintervallen zu respektieren ist. Wir betrachten die Komplexitätsklasse dieses Problems und diskutieren einen auf Werte-Iteration beruhenden Algorithmus um die Pareto-Menge der erreichbaren optimalen Punkte anzunähern. Weiterhin betrachten wir das Problem, minimale und maximale Erreichbarkeitswahrscheinlichkeiten zu berechnen, wenn wir eine obere Grenze für dieakkumulierten Pfadkosten einhalten müssen. Für dieses Problem diskutieren wir einen ef?zienten Algorithmus mit pseudopolynomieller Zeit. Wir zeigen die Ef?zienz unserer Ansätze in der Praxis, indem wir sie prototypisch implementieren und auf eine Reihe von realistischen Fallstudien anwenden