31 research outputs found
Guessing Winning Policies in LTL Synthesis by Semantic Learning
We provide a learning-based technique for guessing a winning strategy in a
parity game originating from an LTL synthesis problem. A cheaply obtained guess
can be useful in several applications. Not only can the guessed strategy be
applied as best-effort in cases where the game's huge size prohibits rigorous
approaches, but it can also increase the scalability of rigorous LTL synthesis
in several ways. Firstly, checking whether a guessed strategy is winning is
easier than constructing one. Secondly, even if the guess is wrong in some
places, it can be fixed by strategy iteration faster than constructing one from
scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize
exploration in the most fruitful directions.
In contrast to previous works, we (i)~reflect the highly structured logical
information in game's states, the so-called semantic labelling, coming from the
recent LTL-to-automata translations, and (ii)~learn to reflect it properly by
learning from previously solved games, bringing the solving process closer to
human-like reasoning
Reasoning about Regular Properties: A Comparative Study
Several new algorithms for deciding emptiness of Boolean combinations of
regular languages and of languages of alternating automata (AFA) have been
proposed recently, especially in the context of analysing regular expressions
and in string constraint solving. The new algorithms demonstrated a significant
potential, but they have never been systematically compared, neither among each
other nor with the state-of-the art implementations of existing
(non)deterministic automata-based methods. In this paper, we provide the first
such comparison as well as an overview of the existing algorithms and their
implementations. We collect a diverse benchmark mostly originating in or
related to practical problems from string constraint solving, analysing LTL
properties, and regular model checking, and evaluate collected implementations
on it. The results reveal the best tools and hint on what the best algorithms
and implementation techniques are. Roughly, although some advanced algorithms
are fast, such as antichain algorithms and reductions to IC3/PDR, they are not
as overwhelmingly dominant as sometimes presented and there is no clear winner.
The simplest NFA-based technology may be actually the best choice, depending on
the problem source and implementation style. Our findings should be highly
relevant for development of these techniques as well as for related fields such
as string constraint solving
Index appearance record with preorders
Transforming ω-automata into parity automata is traditionally done using appearance records. We present an efficient variant of this idea, tailored to Rabin automata, and several optimizations applicable to all appearance records. We compare the methods experimentally and show that our method produces significantly smaller automata than previous approaches
Realizing Omega-regular Hyperproperties
We studied the hyperlogic HyperQPTL, which combines the concepts of trace
relations and -regularity. We showed that HyperQPTL is very expressive,
it can express properties like promptness, bounded waiting for a grant,
epistemic properties, and, in particular, any -regular property. Those
properties are not expressible in previously studied hyperlogics like HyperLTL.
At the same time, we argued that the expressiveness of HyperQPTL is optimal in
a sense that a more expressive logic for -regular hyperproperties would
have an undecidable model checking problem. We furthermore studied the
realizability problem of HyperQPTL. We showed that realizability is decidable
for HyperQPTL fragments that contain properties like promptness. But still, in
contrast to the satisfiability problem, propositional quantification does make
the realizability problem of hyperlogics harder. More specifically, the
HyperQPTL fragment of formulas with a universal-existential propositional
quantifier alternation followed by a single trace quantifier is undecidable in
general, even though the projection of the fragment to HyperLTL has a decidable
realizability problem. Lastly, we implemented the bounded synthesis problem for
HyperQPTL in the prototype tool BoSy. Using BoSy with HyperQPTL specifications,
we have been able to synthesize several resource arbiters. The synthesis
problem of non-linear-time hyperlogics is still open. For example, it is not
yet known how to synthesize systems from specifications given in branching-time
hyperlogics like HyperCTL.Comment: International Conference on Computer Aided Verification (CAV 2020
Approximate automata for omega-regular languages
Automata over infinite words, also known as ω -automata, play a key role in the verification and synthesis of reactive systems. The spectrum of ω -automata is defined by two characteristics: the acceptance condition (e.g. Büchi or parity) and the determinism (e.g., deterministic or nondeterministic) of an automaton. These characteristics play a crucial role in applications of automata theory. For example, certain acceptance conditions can be handled more efficiently than others by dedicated tools and algorithms. Furthermore, some applications, such as synthesis and probabilistic model checking, require that properties are represented as some type of deterministic ω -automata. However, properties cannot always be represented by automata with the desired acceptance condition and determinism.
In this paper we study the problem of approximating linear-time properties by automata in a given class. Our approximation is based on preserving the language up to a user-defined precision given in terms of the size of the finite lasso representation of infinite executions that are preserved. We study the state complexity of different types of approximating automata, and provide constructions for the approximation within different automata classes, for example, for approximating a given automaton by one with a simpler acceptance condition
Explaining Hyperproperty Violations
Hyperproperties relate multiple computation traces to each other. Model checkers for hyperproperties thus return, in case a system model violates the specification, a set of traces as a counterexample. Fixing the erroneous relations between traces in the system that led to the counterexample is a difficult manual effort that highly benefits from additional explanations. In this paper, we present an explanation method for counterexamples to hyperproperties described in the specification logic HyperLTL. We extend Halpern and Pearl's definition of actual causality to sets of traces witnessing the violation of a HyperLTL formula, which allows us to identify the events that caused the violation. We report on the implementation of our method and show that it significantly improves on previous approaches for analyzing counterexamples returned by HyperLTL model checkers
Logical and deep learning methods for temporal reasoning
In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive systems. In Part I, we determine decidability borders for the satisfiability and realizability problem of temporal hyperproperties. Temporal hyperproperties relate multiple computation traces to each other and are expressed in a temporal hyperlogic. In particular, we identify decidable fragments of the highly expressive hyperlogics HyperQPTL and HyperCTL*. As an application, we elaborate on an enforcement mechanism for temporal hyperproperties. We study explicit enforcement algorithms for specifications given as formulas in universally quantified HyperLTL. In Part II, we train a (deep) neural network on the trace generation and realizability problem of linear-time temporal logic (LTL). We consider a method to generate large amounts of additional training data from practical specification patterns. The training data is generated with classical solvers, which provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions such that the neural network generalizes to the semantics of the logic. The neural network can predict solutions even for formulas from benchmarks from the literature on which the classical solver timed out. Additionally, we show that it solves a significant portion of problems from the annual synthesis competition (SYNTCOMP) and even out-of-distribution examples from a recent case study.Diese Arbeit befasst sich mit logischen Methoden und mehrschichtigen Lernmethoden für das zeitabhängige Argumentieren über reaktive Systeme. In Teil I werden die Grenzen der Entscheidbarkeit des Erfüllbarkeits- und des Realisierbarkeitsproblem von temporalen Hypereigenschaften bestimmt. Temporale Hypereigenschaften setzen mehrere Berechnungsspuren zueinander in Beziehung und werden in einer temporalen Hyperlogik ausgedrückt. Insbesondere werden entscheidbare Fragmente der hochexpressiven Hyperlogiken HyperQPTL und HyperCTL* identifiziert. Als Anwendung wird ein Enforcement-Mechanismus für temporale Hypereigenschaften erarbeitet. Explizite Enforcement-Algorithmen für Spezifikationen, die als Formeln in universell quantifiziertem HyperLTL angegeben werden, werden untersucht. In Teil II wird ein (mehrschichtiges) neuronales Netz auf den Problemen der Spurgenerierung und Realisierbarkeit von Linear-zeit Temporallogik (LTL) trainiert. Es wird eine Methode betrachtet, um aus praktischen Spezifikationsmustern große Mengen zusätzlicher Trainingsdaten zu generieren. Die Trainingsdaten werden mit klassischen Solvern generiert, die zu jeder Formel nur eine von vielen möglichen Lösungen liefern. Es wird gezeigt, dass es ausreichend ist, an diesen speziellen Lösungen zu trainieren, sodass das neuronale Netz zur Semantik der Logik generalisiert. Das neuronale Netz kann Lösungen sogar für Formeln aus Benchmarks aus der Literatur vorhersagen, bei denen der klassische Solver eine Zeitüberschreitung hatte. Zusätzlich wird gezeigt, dass das neuronale Netz einen erheblichen Teil der Probleme aus dem jährlichen Synthesewettbewerb (SYNTCOMP) und sogar Beispiele außerhalb der Distribution aus einer aktuellen Fallstudie lösen kann