44 research outputs found
SyGuS-Comp 2016: Results and Analysis
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an
implementation f that meets both a semantic constraint given by a logical
formula in a background theory T, and a syntactic constraint given by
a grammar G, which specifies the allowed set of candidate implementations. Such
a synthesis problem can be formally defined in SyGuS-IF, a language that is
built on top of SMT-LIB.
The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to
facilitate, bring together and accelerate research and development of efficient
solvers for SyGuS by providing a platform for evaluating different synthesis
techniques on a comprehensive set of benchmarks. In this year's competition we
added a new track devoted to programming by examples. This track consisted of
two categories, one using the theory of bit-vectors and one using the theory of
strings. This paper presents and analyses the results of SyGuS-Comp'16.Comment: In Proceedings SYNT 2016, arXiv:1611.07178. arXiv admin note: text
overlap with arXiv:1602.0117
Compositional Algorithms for Succinct Safety Games
We study the synthesis of circuits for succinct safety specifications given
in the AIG format. We show how AIG safety specifications can be decomposed
automatically into sub specifications. Then we propose symbolic compositional
algorithms to solve the synthesis problem compositionally starting for the
sub-specifications. We have evaluated the compositional algorithms on a set of
benchmarks including those proposed for the first synthesis competition
organised in 2014 by the Synthesis Workshop affiliated to the CAV conference.
We show that a large number of benchmarks can be decomposed automatically and
solved more efficiently with the compositional algorithms that we propose in
this paper.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
Synthesizing a Lego Forklift Controller in GR(1): A Case Study
Reactive synthesis is an automated procedure to obtain a
correct-by-construction reactive system from a given specification. GR(1) is a
well-known fragment of linear temporal logic (LTL) where synthesis is possible
using a polynomial symbolic algorithm. We conducted a case study to learn about
the challenges that software engineers may face when using GR(1) synthesis for
the development of a reactive robotic system. In the case study we developed
two variants of a forklift controller, deployed on a Lego robot. The case study
employs LTL specification patterns as an extension of the GR(1) specification
language, an examination of two specification variants for execution
scheduling, traceability from the synthesized controller to constraints in the
specification, and generated counter strategies to support understanding
reasons for unrealizability. We present the specifications we developed, our
observations, and challenges faced during the case study.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
Temporal Stream Logic: Synthesis beyond the Bools
Reactive systems that operate in environments with complex data, such as
mobile apps or embedded controllers with many sensors, are difficult to
synthesize. Synthesis tools usually fail for such systems because the state
space resulting from the discretization of the data is too large. We introduce
TSL, a new temporal logic that separates control and data. We provide a
CEGAR-based synthesis approach for the construction of implementations that are
guaranteed to satisfy a TSL specification for all possible instantiations of
the data processing functions. TSL provides an attractive trade-off for
synthesis. On the one hand, synthesis from TSL, unlike synthesis from standard
temporal logics, is undecidable in general. On the other hand, however,
synthesis from TSL is scalable, because it is independent of the complexity of
the handled data. Among other benchmarks, we have successfully synthesized a
music player Android app and a controller for an autonomous vehicle in the Open
Race Car Simulator (TORCS.
Neural Circuit Synthesis from Specification Patterns
We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study
NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis
We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for
reactive synthesis. At the core of the solver lies a seamless integration of
neural and symbolic approaches to solving the reactive synthesis problem. To
ensure soundness, the neural engine is coupled with model checkers verifying
the predictions of the underlying neural models. The open-source implementation
of NeuroSynt provides an integration framework for reactive synthesis in which
new neural and state-of-the-art symbolic approaches can be seamlessly
integrated. Extensive experiments demonstrate its efficacy in handling
challenging specifications, enhancing the state-of-the-art reactive synthesis
solvers, with NeuroSynt contributing novel solves in the current SYNTCOMP
benchmarks
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
Synthesizing Dominant Strategies for Liveness
Reactive synthesis automatically derives a strategy that satisfies a given specification. However, requiring a strategy to meet the specification in every situation is, in many cases, too hard of a requirement. Particularly in compositional synthesis of distributed systems, individual winning strategies for the processes often do not exist. Remorsefree dominance, a weaker notion than winning, accounts for such situations: dominant strategies are only required to be as good as any alternative strategy, i.e.they are allowed to violate the specification if no other strategy would have satisfied it in the same situation. The composition of dominant strategies is only guaranteed to be dominant for safety properties, though; preventing the use of dominance in compositional synthesis for liveness specifications. Yet, safety properties are often not expressive enough. In this paper, we thus introduce a new winning condition for strategies, called delay-dominance, that overcomes this weakness of remorsefree dominance: we show that it is compositional for many safety and liveness specifications, enabling a compositional synthesis algorithm based on delay-dominance for general specifications. Furthermore, we introduce an automaton construction for recognizing delay-dominant strategies and prove its soundness and completeness. The resulting automaton is of single-exponential size in the squared length of the specification and can immediately be used for safraless synthesis procedures. Thus, synthesis of delay-dominant strategies is, as synthesis of winning strategies, in 2EXPTIME