208 research outputs found
Quantitative multi-objective verification for probabilistic systems
We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies
On finitely ambiguous B\"uchi automata
Unambiguous B\"uchi automata, i.e. B\"uchi automata allowing only one
accepting run per word, are a useful restriction of B\"uchi automata that is
well-suited for probabilistic model-checking. In this paper we propose a more
permissive variant, namely finitely ambiguous B\"uchi automata, a
generalisation where each word has at most accepting runs, for some fixed
. We adapt existing notions and results concerning finite and bounded
ambiguity of finite automata to the setting of -languages and present a
translation from arbitrary nondeterministic B\"uchi automata with states to
finitely ambiguous automata with at most states and at most accepting
runs per word
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Markov decision processes (MDPs) are the standard formalism for modelling
sequential decision making in stochastic environments. Policy synthesis
addresses the problem of how to control or limit the decisions an agent makes
so that a given specification is met. In this paper we consider PCTL*, the
probabilistic counterpart of CTL*, as the specification language. Because in
general the policy synthesis problem for PCTL* is undecidable, we restrict to
policies whose execution history memory is finitely bounded a priori.
Surprisingly, no algorithm for policy synthesis for this natural and
expressive framework has been developed so far. We close this gap and describe
a tableau-based algorithm that, given an MDP and a PCTL* specification, derives
in a non-deterministic way a system of (possibly nonlinear) equalities and
inequalities. The solutions of this system, if any, describe the desired
(stochastic) policies.
Our main result in this paper is the correctness of our method, i.e.,
soundness, completeness and termination.Comment: This is a long version of a conference paper published at TABLEAUX
2017. It contains proofs of the main results and fixes a bug. See the
footnote on page 1 for detail
Reachability in Parametric Interval Markov Chains using Constraints
Parametric Interval Markov Chains (pIMCs) are a specification formalism that
extend Markov Chains (MCs) and Interval Markov Chains (IMCs) by taking into
account imprecision in the transition probability values: transitions in pIMCs
are labeled with parametric intervals of probabilities. In this work, we study
the difference between pIMCs and other Markov Chain abstractions models and
investigate the two usual semantics for IMCs: once-and-for-all and
at-every-step. In particular, we prove that both semantics agree on the
maximal/minimal reachability probabilities of a given IMC. We then investigate
solutions to several parameter synthesis problems in the context of pIMCs --
consistency, qualitative reachability and quantitative reachability -- that
rely on constraint encodings. Finally, we propose a prototype implementation of
our constraint encodings with promising results
From LTL and Limit-Deterministic B\"uchi Automata to Deterministic Parity Automata
Controller synthesis for general linear temporal logic (LTL) objectives is a
challenging task. The standard approach involves translating the LTL objective
into a deterministic parity automaton (DPA) by means of the Safra-Piterman
construction. One of the challenges is the size of the DPA, which often grows
very fast in practice, and can reach double exponential size in the length of
the LTL formula. In this paper we describe a single exponential translation
from limit-deterministic B\"uchi automata (LDBA) to DPA, and show that it can
be concatenated with a recent efficient translation from LTL to LDBA to yield a
double exponential, \enquote{Safraless} LTL-to-DPA construction. We also report
on an implementation, a comparison with the SPOT library, and performance on
several sets of formulas, including instances from the 2016 SyntComp
competition
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
Decision Problems for Nash Equilibria in Stochastic Games
We analyse the computational complexity of finding Nash equilibria in
stochastic multiplayer games with -regular objectives. While the
existence of an equilibrium whose payoff falls into a certain interval may be
undecidable, we single out several decidable restrictions of the problem.
First, restricting the search space to stationary, or pure stationary,
equilibria results in problems that are typically contained in PSPACE and NP,
respectively. Second, we show that the existence of an equilibrium with a
binary payoff (i.e. an equilibrium where each player either wins or loses with
probability 1) is decidable. We also establish that the existence of a Nash
equilibrium with a certain binary payoff entails the existence of an
equilibrium with the same payoff in pure, finite-state strategies.Comment: 22 pages, revised versio
Modular Verification for a Class of PLTL Properties
The verification of dynamic properties of a reactive systems by model-checking leads to a potential combinatorial explosion of the state space that has to be checked. In order to deal with this problem, we define a strategy based on local verifications rather than on a global verification. The idea is to split the system into subsystems called modules, and to verify the properties on each module in separation. We prove for a class of PLTL properties that if a property is satisfied on each module, then it is globally satisfied. We call such properties modular properties. We propose a modular decomposition based on the B refinement process. We present in this paper an usual class of dynamic properties in the shape of G (p -> Q), where `p' is a proposition and `Q' is a simple temporal formula, such as `X q', `F q', or `q U r' (with `q' and `r' being propositions). We prove that these dynamic properties are modular. For these specific patterns, we have exhibited some syntactic conditions of modularity on their corresponding Buchi automata. These conditions define a larger class which contains other patterns such as `G (p -> X (q U r))'. Finally, we show through the example of an industrial Robot that this method is valid in a practical way
Parametric LTL on Markov Chains
This paper is concerned with the verification of finite Markov chains against
parametrized LTL (pLTL) formulas. In pLTL, the until-modality is equipped with
a bound that contains variables; e.g., asserts that
holds within time steps, where is a variable on natural
numbers. The central problem studied in this paper is to determine the set of
parameter valuations for which the probability to
satisfy pLTL-formula in a Markov chain meets a given threshold , where is a comparison on reals and a probability. As for pLTL
determining the emptiness of is undecidable, we consider
several logic fragments. We consider parametric reachability properties, a
sub-logic of pLTL restricted to next and , parametric B\"uchi
properties and finally, a maximal subclass of pLTL for which emptiness of is decidable.Comment: TCS Track B 201
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