32 research outputs found
Model Checking Games for the Quantitative mu-Calculus
We investigate quantitative extensions of modal logic and the modal
mu-calculus, and study the question whether the tight connection between logic
and games can be lifted from the qualitative logics to their quantitative
counterparts. It turns out that, if the quantitative mu-calculus is defined in
an appropriate way respecting the duality properties between the logical
operators, then its model checking problem can indeed be characterised by a
quantitative variant of parity games. However, these quantitative games have
quite different properties than their classical counterparts, in particular
they are, in general, not positionally determined. The correspondence between
the logic and the games goes both ways: the value of a formula on a
quantitative transition system coincides with the value of the associated
quantitative game, and conversely, the values of quantitative parity games are
definable in the quantitative mu-calculus
Lukasiewicz mu-Calculus
We consider state-based systems modelled as coalgebras whose type incorporates branching, and show that by suitably adapting the definition of coalgebraic bisimulation, one obtains a general and uniform account of the linear-time behaviour of a state in such a coalgebra. By moving away from a boolean universe of truth values, our approach can measure the extent to which a state in a system with branching is able to exhibit a particular linear-time behaviour. This instantiates to measuring the probability of a specific behaviour occurring in a probabilistic system, or measuring the minimal cost of exhibiting a specific behaviour in the case of weighted computations
Separable GPL: Decidable Model Checking with More Non-Determinism
Generalized Probabilistic Logic (GPL) is a temporal logic, based on the modal mu-calculus, for specifying properties of branching probabilistic systems. We consider GPL over branching systems that also exhibit internal non-determinism under linear-time semantics (which is resolved by schedulers), and focus on the problem of finding the capacity (supremum probability over all schedulers) of a fuzzy formula. Model checking GPL is undecidable, in general, over such systems, and existing GPL model checking algorithms are limited to systems without internal non-determinism, or to checking non-recursive formulae. We define a subclass, called separable GPL, which includes recursive formulae and for which model checking is decidable. A large class of interesting and decidable problems, such as termination of 1-exit Recursive MDPs, reachability of Branching MDPs, and LTL model checking of MDPs, whose decidability has been studied independently, can be reduced to model checking separable GPL. Thus, GPL is widely applicable and, with a suitable extension of its semantics, yields a uniform framework for studying problems involving systems with non-deterministic and probabilistic behaviors
On the equivalence of game and denotational semantics for the probabilistic mu-calculus
The probabilistic (or quantitative) modal mu-calculus is a fixed-point logic
de- signed for expressing properties of probabilistic labeled transition
systems (PLTS). Two semantics have been studied for this logic, both assigning
to every process state a value in the interval [0,1] representing the
probability that the property expressed by the formula holds at the state. One
semantics is denotational and the other is a game semantics, specified in terms
of two-player stochastic games. The two semantics have been proved to coincide
on all finite PLTS's, but the equivalence of the two semantics on arbitrary
models has been open in literature. In this paper we prove that the equivalence
indeed holds for arbitrary infinite models, and thus our result strengthens the
fruitful connection between denotational and game semantics. Our proof adapts
the unraveling or unfolding method, a general proof technique for proving
result of parity games by induction on their complexity
Approximating a Behavioural Pseudometric without Discount for<br> Probabilistic Systems
Desharnais, Gupta, Jagadeesan and Panangaden introduced a family of
behavioural pseudometrics for probabilistic transition systems. These
pseudometrics are a quantitative analogue of probabilistic bisimilarity.
Distance zero captures probabilistic bisimilarity. Each pseudometric has a
discount factor, a real number in the interval (0, 1]. The smaller the discount
factor, the more the future is discounted. If the discount factor is one, then
the future is not discounted at all. Desharnais et al. showed that the
behavioural distances can be calculated up to any desired degree of accuracy if
the discount factor is smaller than one. In this paper, we show that the
distances can also be approximated if the future is not discounted. A key
ingredient of our algorithm is Tarski's decision procedure for the first order
theory over real closed fields. By exploiting the Kantorovich-Rubinstein
duality theorem we can restrict to the existential fragment for which more
efficient decision procedures exist
PCTL Model Checking of Markov Chains: Truth and Falsity as Winning Strategies in Games
Probabilistic model checking is a technique for verifying whether a model such as a Markov chain satisfies a probabilistic, behavioral property â e.g. âwith probability at least 0.999, a device will be elected leader. â Such properties are expressible in probabilistic temporal logics, e.g. PCTL, and efficient algorithms exist for checking whether these formulae are true or false on finite-state models. Alas, these algorithms donât supply diagnostic information for why a probabilistic property does or does not hold in a given model. We provide here complete and rigorous foundations for such diagnostics in the setting of countable labeled Markov chains and PCTL. For each model and PCTL formula, we define a game between a Verifier and a Refuter that is won by Verifier if the formula holds in the model, and won by Refuter if it doesnât hold. Games are won by exactly one player, through monotone strategies that encode the diagnostic information for truth and falsity (respectively). These games are infinite with BĂŒchi type acceptance conditions where simpler fairness conditions are shown not be to sufficient. Verifier can always force finite plays for certain PCTL formulae, suggesting the existence of finite-state abstractions of models that satisfy such formulae