3,859 research outputs found
An Introduction to Pervasive Interface Automata
Pervasive systems are often context-dependent, component based systems in which components expose interfaces and offer one or more services. These systems may evolve in unpredictable ways, often through component replacement. We present pervasive interface automata as a formalism for modelling components and their composition. Pervasive interface automata are based on the interface automata of Henzinger et al, with several significant differences. We expand their notion of input and output actions to combinations of input, output actions, and callable methods and method calls. Whereas interfaces automata have a refinement relation, we argue the crucial relation in pervasive systems is component replacement, which must include consideration of the services offered by a component and assumptions about the environment. We illustrate pervasive interface autmotata and component replacement with a small case study of a pervasive application for sports predictions
An Algorithm for Probabilistic Alternating Simulation
In probabilistic game structures, probabilistic alternating simulation
(PA-simulation) relations preserve formulas defined in probabilistic
alternating-time temporal logic with respect to the behaviour of a subset of
players. We propose a partition based algorithm for computing the largest
PA-simulation, which is to our knowledge the first such algorithm that works in
polynomial time, by extending the generalised coarsest partition problem (GCPP)
in a game-based setting with mixed strategies. The algorithm has higher
complexities than those in the literature for non-probabilistic simulation and
probabilistic simulation without mixed actions, but slightly improves the
existing result for computing probabilistic simulation with respect to mixed
actions.Comment: We've fixed a problem in the SOFSEM'12 conference versio
Computing Quantiles in Markov Reward Models
Probabilistic model checking mainly concentrates on techniques for reasoning
about the probabilities of certain path properties or expected values of
certain random variables. For the quantitative system analysis, however, there
is also another type of interesting performance measure, namely quantiles. A
typical quantile query takes as input a lower probability bound p and a
reachability property. The task is then to compute the minimal reward bound r
such that with probability at least p the target set will be reached before the
accumulated reward exceeds r. Quantiles are well-known from mathematical
statistics, but to the best of our knowledge they have not been addressed by
the model checking community so far.
In this paper, we study the complexity of quantile queries for until
properties in discrete-time finite-state Markov decision processes with
non-negative rewards on states. We show that qualitative quantile queries can
be evaluated in polynomial time and present an exponential algorithm for the
evaluation of quantitative quantile queries. For the special case of Markov
chains, we show that quantitative quantile queries can be evaluated in time
polynomial in the size of the chain and the maximum reward.Comment: 17 pages, 1 figure; typo in example correcte
A Few Considerations on Structural and Logical Composition in Specification Theories
Over the last 20 years a large number of automata-based specification
theories have been proposed for modeling of discrete,real-time and
probabilistic systems. We have observed a lot of shared algebraic structure
between these formalisms. In this short abstract, we collect results of our
work in progress on describing and systematizing the algebraic assumptions in
specification theories.Comment: In Proceedings FIT 2010, arXiv:1101.426
ECDAR: An Environment for Compositional Design and Analysis of Real Time Systems
Abstract. We present Ecdar a new tool for compositional design and verification of real time systems. In Ecdar, a component interface de-scribes both the behavior of the component and the component’s assump-tions about the environment. The tool supports the important operations of a good compositional reasoning theory: composition, conjunction, quo-tient, consistency/satisfaction checking, and refinement. The operators can be used to combine basic models into larger specifications to con-struct comprehensive system descriptions from basic requirements. Algo-rithms to perform these operations have been based on a game theoretical setting that permits, for example, to capture the real-time constraints on communication events between components. The compositional ap-proach allows for scalability in the verification.
Mean-payoff Automaton Expressions
Quantitative languages are an extension of boolean languages that assign to
each word a real number. Mean-payoff automata are finite automata with
numerical weights on transitions that assign to each infinite path the long-run
average of the transition weights. When the mode of branching of the automaton
is deterministic, nondeterministic, or alternating, the corresponding class of
quantitative languages is not robust as it is not closed under the pointwise
operations of max, min, sum, and numerical complement. Nondeterministic and
alternating mean-payoff automata are not decidable either, as the quantitative
generalization of the problems of universality and language inclusion is
undecidable.
We introduce a new class of quantitative languages, defined by mean-payoff
automaton expressions, which is robust and decidable: it is closed under the
four pointwise operations, and we show that all decision problems are decidable
for this class. Mean-payoff automaton expressions subsume deterministic
mean-payoff automata, and we show that they have expressive power incomparable
to nondeterministic and alternating mean-payoff automata. We also present for
the first time an algorithm to compute distance between two quantitative
languages, and in our case the quantitative languages are given as mean-payoff
automaton expressions
Discounting in LTL
In recent years, there is growing need and interest in formalizing and
reasoning about the quality of software and hardware systems. As opposed to
traditional verification, where one handles the question of whether a system
satisfies, or not, a given specification, reasoning about quality addresses the
question of \emph{how well} the system satisfies the specification. One
direction in this effort is to refine the "eventually" operators of temporal
logic to {\em discounting operators}: the satisfaction value of a specification
is a value in , where the longer it takes to fulfill eventuality
requirements, the smaller the satisfaction value is.
In this paper we introduce an augmentation by discounting of Linear Temporal
Logic (LTL), and study it, as well as its combination with propositional
quality operators. We show that one can augment LTL with an arbitrary set of
discounting functions, while preserving the decidability of the model-checking
problem. Further augmenting the logic with unary propositional quality
operators preserves decidability, whereas adding an average-operator makes some
problems undecidable. We also discuss the complexity of the problem, as well as
various extensions
Equilibria-based Probabilistic Model Checking for Concurrent Stochastic Games
Probabilistic model checking for stochastic games enables formal verification
of systems that comprise competing or collaborating entities operating in a
stochastic environment. Despite good progress in the area, existing approaches
focus on zero-sum goals and cannot reason about scenarios where entities are
endowed with different objectives. In this paper, we propose probabilistic
model checking techniques for concurrent stochastic games based on Nash
equilibria. We extend the temporal logic rPATL (probabilistic alternating-time
temporal logic with rewards) to allow reasoning about players with distinct
quantitative goals, which capture either the probability of an event occurring
or a reward measure. We present algorithms to synthesise strategies that are
subgame perfect social welfare optimal Nash equilibria, i.e., where there is no
incentive for any players to unilaterally change their strategy in any state of
the game, whilst the combined probabilities or rewards are maximised. We
implement our techniques in the PRISM-games tool and apply them to several case
studies, including network protocols and robot navigation, showing the benefits
compared to existing approaches
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