Results 1  10
of
52
Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification
, 2010
"... We address the problem of model checking stochastic systems, i.e. checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a novel Statistical Model Checking (SMC) approach based on Bayesian s ..."
Abstract

Cited by 46 (9 self)
 Add to MetaCart
(Show Context)
We address the problem of model checking stochastic systems, i.e. checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a novel Statistical Model Checking (SMC) approach based on Bayesian statistics. We show that our approach is feasible for hybrid systems with stochastic transitions, a generalization of Simulink/Stateflow models. Standard approaches to stochastic (discrete) systems require numerical solutions for large optimization problems and quickly become infeasible with larger state spaces. Generalizations of these techniques to hybrid systems with stochastic effects are even more challenging. The SMC approach was pioneered by Younes and Simmons in the discrete and nonBayesian case. It solves the verification problem by combining randomized sampling of system traces (which is very efficient for Simulink/Stateflow) with hypothesis testing or estimation. We believe SMC is essential for scaling up to large Stateflow/Simulink models. While the answer to the verification problem is not guaranteed to be correct, we prove that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small. The advantage is that answers can usually be obtained much faster than with standard, exhaustive model checking
Logics of Dynamical Systems
"... We study the logic of dynamical systems, that is, logics and proof principles for properties of dynamical systems. Dynamical systems are mathematical models describing how the state of a system evolves over time. They are important in modeling and understanding many applications, including embedded ..."
Abstract

Cited by 18 (17 self)
 Add to MetaCart
We study the logic of dynamical systems, that is, logics and proof principles for properties of dynamical systems. Dynamical systems are mathematical models describing how the state of a system evolves over time. They are important in modeling and understanding many applications, including embedded systems and cyberphysical systems. In discrete dynamical systems, the state evolves in discrete steps, one step at a time, as described by a difference equation or discrete state transition relation. In continuous dynamical systems, the state evolves continuously along a function, typically described by a differential equation. Hybrid dynamical systems or hybrid systems combine both discrete and continuous dynamics. Distributed hybrid systems combine distributed systems with hybrid systems, i.e., they are multiagent hybrid systems that interact through remote communication or physical interaction. Stochastic hybrid systems combine stochastic
Static Analysis for Probabilistic Programs: Inferring Whole Program Properties from Finitely Many Paths.
"... We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyberphysical systems. Correctness properties of such programs take the form of queries ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
(Show Context)
We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyberphysical systems. Correctness properties of such programs take the form of queries that seek the probabilities of assertions over program variables. We present a static analysis approach that provides guaranteed interval bounds on the values (assertion probabilities) of such queries. First, we observe that for probabilistic programs, it is possible to conclude facts about the behavior of the entire program by choosing a finite, adequate set of its paths. We provide strategies for choosing such a set of paths and verifying its adequacy. The queries are evaluated over each path by a combination of symbolic execution and probabilistic volumebound computations. Each path yields interval bounds that can be summed up with a “coverage ” bound to yield an interval that encloses the probability of assertion for the program as a whole. We demonstrate promising results on a suite of benchmarks from many different sources including robotic manipulators and medical decision making programs.
Fluid model checking
 in: Proceedings of CONCUR 2012
, 2012
"... In this paper we investigate a potential use of fluid approximation techniques in the context of stochastic model checking of CSL formulae. We focus on properties describing the behaviour of a single agent in a (large) population of agents, exploiting a limit result known also as fast simulation. In ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
In this paper we investigate a potential use of fluid approximation techniques in the context of stochastic model checking of CSL formulae. We focus on properties describing the behaviour of a single agent in a (large) population of agents, exploiting a limit result known also as fast simulation. In particular, we will approximate the behaviour of a single agent with a timeinhomogeneous CTMC, which depends on the environment and on the other agents only through the solution of the fluid differential equation, and model check this process. We will prove the asymptotic correctness of our approach in terms of satisfiability of CSL formulae. We will also present a procedure to model check timeinhomogeneous CTMC against CSL formulae.
Generalized Queries and Bayesian Statistical Model Checking in Dynamic Bayesian Networks: Application to Personalized Medicine
 In: Proc. 8th Ann. Intnl Conf. on Comput. Sys. Bioinf. (CSB
, 2009
"... We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional query types for DBNs (i.e., filtering, smoothing, prediction, and classification), but can also be used to express inference problems that are either impossible, or impractical to answer using traditional algorithms for inference in DBNs. We then discuss the relationship between answering generalized queries and the Probabilistic Model Checking Problem and introduce two novel algorithms for efficiently estimating P (φ1φ2) in a Bayesian fashion. Finally, we demonstrate our method by answering generalized queries that arise in the context of critical care medicine. Specifically, we show that our approach can be used to make treatment decisions for a cohort of 1,000 simulated sepsis patients, and that it outperforms Support Vector Machines, Neural Networks, and Random Forests on the same task.
Fainekos. Simulating Insulin Infusion Pump Risks by InSilico Modeling
 of the InsulinGlucose Regulatory System. CMSB
, 2012
"... Abstract. We present a casestudy on the use of robustnessguided and statistical model checking approaches for simulating risks due to insulin infusion pump usage by diabetic patients. Insulin infusion pumps allow for a continuous delivery of insulin with varying rates and delivery profiles to help ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
(Show Context)
Abstract. We present a casestudy on the use of robustnessguided and statistical model checking approaches for simulating risks due to insulin infusion pump usage by diabetic patients. Insulin infusion pumps allow for a continuous delivery of insulin with varying rates and delivery profiles to help patients selfregulate their blood glucose levels. However, the use of infusion pumps and continuous glucose monitors can pose risks to the patient including chronically elevated blood glucose levels (hyperglycemia) or dangerously low glucose levels (hypoglycemia). In this paper, we use mathematical models of the basic insulinglucose regulatory system in a diabetic patient, insulin infusion pumps, and the user’s interaction with these pumps defined by commonly used insulin infusion strategies for maintaining normal glucose levels. These strategies include common guidelines taught to patients by physicians and certified diabetes educators and have been implemented in commercially available insulin bolus calculators. Furthermore, we model the failures in the devices themselves along with common errors in the usage of the pump. We compose these models together and analyze them using two related techniques: (a) robustness guided statespace search to explore worstcase scenarios and (b) statistical model checking techniques to assess the probabilities of hyper and hypoglycemia risks. Our technique can be used to identify the worstcase effects of the combination of many different kinds of failures and place high confidence bounds on their probabilities. 1
A.: Formulafree finite abstractions for linear temporal verification of stochastic hybrid systems
 In: Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control
, 2013
"... Results on approximate modelchecking of Stochastic Hybrid Systems (SHS) against general temporal specifications lead to abstractions that structurally depend on the given specification or with a state cardinality that crucially depends on the size of the specification. In order to cope with the a ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
(Show Context)
Results on approximate modelchecking of Stochastic Hybrid Systems (SHS) against general temporal specifications lead to abstractions that structurally depend on the given specification or with a state cardinality that crucially depends on the size of the specification. In order to cope with the associated issues of generality and scalability, we propose a specificationfree abstraction approach that is general, namely it allows constructing a single abstraction to be then used for a whole cohort of problems. It furthermore computationally outperforms specificationdependent abstractions over linear temporal properties, such as bounded LTL (BLTL). The proposed approach unifies techniques for the approximate abstraction of SHS over different classes of properties by explicitly relating the error introduced by the approximation to the distance between transition kernels of abstract and concrete models, and by propagating the error in time over the horizon of the specification. The new technique is compared over a case study to related results in the literature.
Cross entropy optimisation of importance sampling parameters for statistical model checking
 In CAV
, 2012
"... Abstract. Statistical model checking avoids the exponential growth of states associated with probabilistic model checking by estimating probabilities from multiple executions of a system and by giving results within confidence bounds. Rare properties are often important but pose a particular challe ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
Abstract. Statistical model checking avoids the exponential growth of states associated with probabilistic model checking by estimating probabilities from multiple executions of a system and by giving results within confidence bounds. Rare properties are often important but pose a particular challenge for simulationbased approaches, hence a key objective for statistical model checking (SMC) is to reduce the number and length of simulations necessary to produce a result with a given level of confidence. Importance sampling can achieves this, however to maintain the advantages of SMC it is necessary to find good importance sampling distributions without considering the entire state space. Here we present a simple algorithm that uses the notion of crossentropy to find an optimal importance sampling distribution. In contrast to previous work, our algorithm uses a naturally defined low dimensional vector of parameters to specify this distribution and thus avoids the intractable explicit representation of a transition matrix. We show that our parametrisation leads to a unique optimum and can produce many orders of magnitude improvement in simulation efficiency. We demonstrate the efficacy of our methodology by applying it to models from reliability engineering and biochemistry.
Verification of an afdx infrastructure using simulations and probabilities. volume 6418 of LNCS
, 2010
"... Abstract. Until recently, there was not a strong need for networking inside aircrafts. Indeed, the communications were mainly cabled and handled by Ethernet protocols. The evolution of avionics embedded systems and the number of integrated functions in civilian aircrafts has changed the situation. I ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
Abstract. Until recently, there was not a strong need for networking inside aircrafts. Indeed, the communications were mainly cabled and handled by Ethernet protocols. The evolution of avionics embedded systems and the number of integrated functions in civilian aircrafts has changed the situation. Indeed, those functionalities implies a huge increase in the quantity of data exchanged and thus in the number of connections between functions. Among the available mechanisms provided to handle this new complexity, one find Avionics Full Duplex Switched Ethernet (AFDX), a protocol that allows to simulate a pointtopoint network between a source and one or more destinations. The core idea in AFDX is the one of Virtual Links (VL) that are used to simulate pointtopoint communication between devices. One of the main challenge is to show that the total delivery time for packets on VL is bounded by some predefined value. This is a difficult problem that also requires to provide a formal, but quite evolutive, model of the AFDX network. In this paper, we propose to use a componentbased design methodology to describe the behavior of the model. We then propose a stochastic abstraction that allows not only to simplify the complexity of the verification process but also to provide quantitative information on the protocol. 1
Statistical Model Checking for Distributed ProbabilisticControl Hybrid Automata with Smart Grid Applications
, 2011
"... This technical report is a more detailed version of a published paper [12]. The power industry is currently moving towards a more dynamical, intelligent power grid. This Smart Grid is still in its infancy and a formal evaluation of the expensive technologies and ideas on the table is necessary befor ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
This technical report is a more detailed version of a published paper [12]. The power industry is currently moving towards a more dynamical, intelligent power grid. This Smart Grid is still in its infancy and a formal evaluation of the expensive technologies and ideas on the table is necessary before committing to a full investment. In this paper, we argue that a good model for the Smart Grid must match its basic properties: it must be hybrid (both evolve over time, and perform control/computation), distributed (multiple concurrently executing entities), and allow for asynchronous communication and stochastic behaviour (to accurately model realworld power consumption). We propose Distributed ProbabilisticControl Hybrid Automata (DPCHA) as a model for this purpose, and extend Bounded LTL to Quantified Bounded LTL in order to adapt and apply existing statistical modelchecking techniques. We provide an implementation of a framework for developing and verifying DPCHAs. Finally, we conduct a case study for Smart Grid communications analysis. Keywords: statistical model checking, hybrid automata, hybrid systems, power