2,960 research outputs found
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
Real-time and Probabilistic Temporal Logics: An Overview
Over the last two decades, there has been an extensive study on logical
formalisms for specifying and verifying real-time systems. Temporal logics have
been an important research subject within this direction. Although numerous
logics have been introduced for the formal specification of real-time and
complex systems, an up to date comprehensive analysis of these logics does not
exist in the literature. In this paper we analyse real-time and probabilistic
temporal logics which have been widely used in this field. We extrapolate the
notions of decidability, axiomatizability, expressiveness, model checking, etc.
for each logic analysed. We also provide a comparison of features of the
temporal logics discussed
Decisive Markov Chains
We consider qualitative and quantitative verification problems for
infinite-state Markov chains. We call a Markov chain decisive w.r.t. a given
set of target states F if it almost certainly eventually reaches either F or a
state from which F can no longer be reached. While all finite Markov chains are
trivially decisive (for every set F), this also holds for many classes of
infinite Markov chains. Infinite Markov chains which contain a finite attractor
are decisive w.r.t. every set F. In particular, this holds for probabilistic
lossy channel systems (PLCS). Furthermore, all globally coarse Markov chains
are decisive. This class includes probabilistic vector addition systems (PVASS)
and probabilistic noisy Turing machines (PNTM). We consider both safety and
liveness problems for decisive Markov chains, i.e., the probabilities that a
given set of states F is eventually reached or reached infinitely often,
respectively. 1. We express the qualitative problems in abstract terms for
decisive Markov chains, and show an almost complete picture of its decidability
for PLCS, PVASS and PNTM. 2. We also show that the path enumeration algorithm
of Iyer and Narasimha terminates for decisive Markov chains and can thus be
used to solve the approximate quantitative safety problem. A modified variant
of this algorithm solves the approximate quantitative liveness problem. 3.
Finally, we show that the exact probability of (repeatedly) reaching F cannot
be effectively expressed (in a uniform way) in Tarski-algebra for either PLCS,
PVASS or (P)NTM.Comment: 32 pages, 0 figure
Model Checking Probabilistic Pushdown Automata
We consider the model checking problem for probabilistic pushdown automata
(pPDA) and properties expressible in various probabilistic logics. We start
with properties that can be formulated as instances of a generalized random
walk problem. We prove that both qualitative and quantitative model checking
for this class of properties and pPDA is decidable. Then we show that model
checking for the qualitative fragment of the logic PCTL and pPDA is also
decidable. Moreover, we develop an error-tolerant model checking algorithm for
PCTL and the subclass of stateless pPDA. Finally, we consider the class of
omega-regular properties and show that both qualitative and quantitative model
checking for pPDA is decidable
Verifying nondeterministic probabilistic channel systems against -regular linear-time properties
Lossy channel systems (LCSs) are systems of finite state automata that
communicate via unreliable unbounded fifo channels. In order to circumvent the
undecidability of model checking for nondeterministic
LCSs, probabilistic models have been introduced, where it can be decided
whether a linear-time property holds almost surely. However, such fully
probabilistic systems are not a faithful model of nondeterministic protocols.
We study a hybrid model for LCSs where losses of messages are seen as faults
occurring with some given probability, and where the internal behavior of the
system remains nondeterministic. Thus the semantics is in terms of
infinite-state Markov decision processes. The purpose of this article is to
discuss the decidability of linear-time properties formalized by formulas of
linear temporal logic (LTL). Our focus is on the qualitative setting where one
asks, e.g., whether a LTL-formula holds almost surely or with zero probability
(in case the formula describes the bad behaviors). Surprisingly, it turns out
that -- in contrast to finite-state Markov decision processes -- the
satisfaction relation for LTL formulas depends on the chosen type of schedulers
that resolve the nondeterminism. While all variants of the qualitative LTL
model checking problem for the full class of history-dependent schedulers are
undecidable, the same questions for finite-memory scheduler can be solved
algorithmically. However, the restriction to reachability properties and
special kinds of recurrent reachability properties yields decidable
verification problems for the full class of schedulers, which -- for this
restricted class of properties -- are as powerful as finite-memory schedulers,
or even a subclass of them.Comment: 39 page
Probabilistic regular graphs
Deterministic graph grammars generate regular graphs, that form a structural
extension of configuration graphs of pushdown systems. In this paper, we study
a probabilistic extension of regular graphs obtained by labelling the terminal
arcs of the graph grammars by probabilities. Stochastic properties of these
graphs are expressed using PCTL, a probabilistic extension of computation tree
logic. We present here an algorithm to perform approximate verification of PCTL
formulae. Moreover, we prove that the exact model-checking problem for PCTL on
probabilistic regular graphs is undecidable, unless restricting to qualitative
properties. Our results generalise those of EKM06, on probabilistic pushdown
automata, using similar methods combined with graph grammars techniques.Comment: In Proceedings INFINITY 2010, arXiv:1010.611
Model-checking branching-time properties of probabilistic automata and probabilistic one-counter automata
This paper studies the problem of model-checking of probabilistic automaton
and probabilistic one-counter automata against probabilistic branching-time
temporal logics (PCTL and PCTL). We show that it is undecidable for these
problems.
We first show, by reducing to emptiness problem of probabilistic automata,
that the model-checking of probabilistic finite automata against branching-time
temporal logics are undecidable. And then, for each probabilistic automata, by
constructing a probabilistic one-counter automaton with the same behavior as
questioned probabilistic automata the undecidability of model-checking problems
against branching-time temporal logics are derived, herein.Comment: Comments are welcom
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