11,542 research outputs found
A Theory of Sampling for Continuous-time Metric Temporal Logic
This paper revisits the classical notion of sampling in the setting of
real-time temporal logics for the modeling and analysis of systems. The
relationship between the satisfiability of Metric Temporal Logic (MTL) formulas
over continuous-time models and over discrete-time models is studied. It is
shown to what extent discrete-time sequences obtained by sampling
continuous-time signals capture the semantics of MTL formulas over the two time
domains. The main results apply to "flat" formulas that do not nest temporal
operators and can be applied to the problem of reducing the verification
problem for MTL over continuous-time models to the same problem over
discrete-time, resulting in an automated partial practically-efficient
discretization technique.Comment: Revised version, 43 pages
Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models
Matlab/Simulink is a development and simulation language that is widely used
by the Cyber-Physical System (CPS) industry to model dynamical systems. There
are two mainstream approaches to verify CPS Simulink models: model testing that
attempts to identify failures in models by executing them for a number of
sampled test inputs, and model checking that attempts to exhaustively check the
correctness of models against some given formal properties. In this paper, we
present an industrial Simulink model benchmark, provide a categorization of
different model types in the benchmark, describe the recurring logical patterns
in the model requirements, and discuss the results of applying model checking
and model testing approaches to identify requirements violations in the
benchmarked models. Based on the results, we discuss the strengths and
weaknesses of model testing and model checking. Our results further suggest
that model checking and model testing are complementary and by combining them,
we can significantly enhance the capabilities of each of these approaches
individually. We conclude by providing guidelines as to how the two approaches
can be best applied together.Comment: 10 pages + 2 page reference
Improving HyLTL model checking of hybrid systems
The problem of model-checking hybrid systems is a long-time challenge in the
scientific community. Most of the existing approaches and tools are either
limited on the properties that they can verify, or restricted to simplified
classes of systems. To overcome those limitations, a temporal logic called
HyLTL has been recently proposed. The model checking problem for this logic has
been solved by translating the formula into an equivalent hybrid automaton,
that can be analized using existing tools. The original construction employs a
declarative procedure that generates exponentially many states upfront, and can
be very inefficient when complex formulas are involved. In this paper we solve
a technical issue in the construction that was not considered in previous
works, and propose a new algorithm to translate HyLTL into hybrid automata,
that exploits optimized techniques coming from the discrete LTL community to
build smaller automata.Comment: In Proceedings GandALF 2013, arXiv:1307.416
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Non-null Infinitesimal Micro-steps: a Metric Temporal Logic Approach
Many systems include components interacting with each other that evolve with
possibly very different speeds. To deal with this situation many formal models
adopt the abstraction of "zero-time transitions", which do not consume time.
These however have several drawbacks in terms of naturalness and logic
consistency, as a system is modeled to be in different states at the same time.
We propose a novel approach that exploits concepts from non-standard analysis
to introduce a notion of micro- and macro-steps in an extension of the TRIO
metric temporal logic, called X-TRIO. We use X-TRIO to provide a formal
semantics and an automated verification technique to Stateflow-like notations
used in the design of flexible manufacturing systems.Comment: 20 pages, 2 figures, submitted to the conference "FORMATS: Formal
Modelling and Analysis of Timed Systems" 201
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
On the connections between PCTL and Dynamic Programming
Probabilistic Computation Tree Logic (PCTL) is a well-known modal logic which
has become a standard for expressing temporal properties of finite-state Markov
chains in the context of automated model checking. In this paper, we give a
definition of PCTL for noncountable-space Markov chains, and we show that there
is a substantial affinity between certain of its operators and problems of
Dynamic Programming. After proving some uniqueness properties of the solutions
to the latter, we conclude the paper with two examples to show that some
recovery strategies in practical applications, which are naturally stated as
reach-avoid problems, can be actually viewed as particular cases of PCTL
formulas.Comment: Submitte
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