2,213 research outputs found
Fluent temporal logic for discrete-time event-based models
Fluent model checking is an automated technique for verifying that an event-based operational model satisfies some state-based declarative properties. The link between the event-based and state-based formalisms is defined through fluents which are state predicates whose value are determined by the occurrences of initiating and terminating events that make the fluents values become true or false, respectively. The existing fluent temporal logic is convenient for reasoning about untimed event-based models but difficult to use for timed models. The paper extends fluent temporal logic with temporal operators for modelling timed properties of discrete-time event-based models. It presents two approaches that differ on whether the properties model the system state after the occurrence of each event or at a fixed time rate. Model checking of timed properties is made possible by translating them into the existing untimed framework. Copyright 2005 ACM
Parameterized Synthesis
We study the synthesis problem for distributed architectures with a
parametric number of finite-state components. Parameterized specifications
arise naturally in a synthesis setting, but thus far it was unclear how to
detect realizability and how to perform synthesis in a parameterized setting.
Using a classical result from verification, we show that for a class of
specifications in indexed LTL\X, parameterized synthesis in token ring networks
is equivalent to distributed synthesis in a network consisting of a few copies
of a single process. Adapting a well-known result from distributed synthesis,
we show that the latter problem is undecidable. We describe a semi-decision
procedure for the parameterized synthesis problem in token rings, based on
bounded synthesis. We extend the approach to parameterized synthesis in
token-passing networks with arbitrary topologies, and show applicability on a
simple case study. Finally, we sketch a general framework for parameterized
synthesis based on cutoffs and other parameterized verification techniques.Comment: Extended version of TACAS 2012 paper, 29 page
Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude
Pervasive user-centric applications are systems which are meant to sense the
presence, mood, and intentions of users in order to optimize user comfort and
performance. Building such applications requires not only state-of-the art
techniques from artificial intelligence but also sound software engineering
methods for facilitating modular design, runtime adaptation and verification of
critical system requirements.
In this paper we focus on high-level design and analysis, and use the
algebraic rewriting language Real-Time Maude for specifying applications in a
real-time setting. We propose a generic component-based approach for modeling
pervasive user-centric systems and we show how to analyze and prove crucial
properties of the system architecture through model checking and simulation.
For proving time-dependent properties we use Metric Temporal Logic (MTL) and
present analysis algorithms for model checking two subclasses of MTL formulas:
time-bounded response and time-bounded safety MTL formulas. The underlying idea
is to extend the Real-Time Maude model with suitable clocks, to transform the
MTL formulas into LTL formulas over the extended specification, and then to use
the LTL model checker of Maude. It is shown that these analyses are sound and
complete for maximal time sampling. The approach is illustrated by a simple
adaptive advertising scenario in which an adaptive advertisement display can
react to actions of the users in front of the display.Comment: In Proceedings RTRTS 2010, arXiv:1009.398
LTL Parameter Synthesis of Parametric Timed Automata
The parameter synthesis problem for parametric timed automata is undecidable
in general even for very simple reachability properties. In this paper we
introduce restrictions on parameter valuations under which the parameter
synthesis problem is decidable for LTL properties. The investigated bounded
integer parameter synthesis problem could be solved using an explicit
enumeration of all possible parameter valuations. We propose an alternative
symbolic zone-based method for this problem which results in a faster
computation. Our technique extends the ideas of the automata-based approach to
LTL model checking of timed automata. To justify the usefulness of our
approach, we provide experimental evaluation and compare our method with
explicit enumeration technique.Comment: 23 pages, extended versio
Parallel symbolic state-space exploration is difficult, but what is the alternative?
State-space exploration is an essential step in many modeling and analysis
problems. Its goal is to find the states reachable from the initial state of a
discrete-state model described. The state space can used to answer important
questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a
starting point for sophisticated investigations expressed in temporal logic.
Unfortunately, the state space is often so large that ordinary explicit data
structures and sequential algorithms cannot cope, prompting the exploration of
(1) parallel approaches using multiple processors, from simple workstation
networks to shared-memory supercomputers, to satisfy large memory and runtime
requirements and (2) symbolic approaches using decision diagrams to encode the
large structured sets and relations manipulated during state-space generation.
Both approaches have merits and limitations. Parallel explicit state-space
generation is challenging, but almost linear speedup can be achieved; however,
the analysis is ultimately limited by the memory and processors available.
Symbolic methods are a heuristic that can efficiently encode many, but not all,
functions over a structured and exponentially large domain; here the pitfalls
are subtler: their performance varies widely depending on the class of decision
diagram chosen, the state variable order, and obscure algorithmic parameters.
As symbolic approaches are often much more efficient than explicit ones for
many practical models, we argue for the need to parallelize symbolic
state-space generation algorithms, so that we can realize the advantage of both
approaches. This is a challenging endeavor, as the most efficient symbolic
algorithm, Saturation, is inherently sequential. We conclude by discussing
challenges, efforts, and promising directions toward this goal
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