Using PRISM model checker as a validation tool for an analytical model of IEEE 802.15.4 networks

Commonly, simulation by using an existing network simulation tool or a simulator developed from scratch is employed for validation of analytical network performance models. An analytical model of star-shaped wireless sensor networks has been proposed in the literature in which, upon receiving a query from the coordinator, each sensor node sends one data frame to it by executing the IEEE 802.15.4 unslotted carrier-sense multiple access with collision avoidance algorithm. The model consists of expressions for calculation of the probability of successful receipt of the data at a certain time and the like. The authors of the model have written a special simulation program in order to validate the expressions. Our aim was to employ probabilistic model checker PRISM instead. PRISM only requires the user to formally specify the network as a kind of state machine and the queries about the probabilities sought in the form of logical formulas. It finds the probabilities automatically and can present them on graphs. We show how to specify the networks formally in such a way that all the expressions from the analytical model can be validated with PRISM. For those networks containing a few nodes, the validation can be carried out by normal model checking, which, in contrast to the simulation, always checks all the possible network behaviors, whereas statistical model checking can be used for the larger networks.     

Probabilistic symbolic model checking with PRISM: a hybrid approach

In this paper we present efficient symbolic techniques for probabilistic model checking. These have been implemented in PRISM, a tool for the analysis of probabilistic models such as discrete-time Markov chains, continuous-time Markov chains and Markov decision processes using specifications in the probabilistic temporal logics PCTL and CSL. Motivated by the success of model checkers such as SMV which use BDDs (binary decision diagrams), we have developed an implementation of PCTL and CSL model checking based on MTBDDs (multi-terminal BDDs) and BDDs. Existing work in this direction has been hindered by the generally poor performance of MTBDD-based numerical computation, which is often substantially slower than explicit methods using sparse matrices. The focus of this paper is a novel hybrid technique which combines aspects of symbolic and explicit approaches to overcome these performance problems. For typical examples, we achieve a dramatic improvement over the purely symbolic approach. In addition, thanks to the compact model representation using MTBDDs, we can verify systems an order of magnitude larger than with sparse matrices, while almost matching or even beating them for speed.     

Model checking probabilistic social commitments for intelligent agent communication

Interaction among autonomous agents in Multi-Agent Systems (MASs) is a key aspect for agents to coordinate with one another. Social approaches, as opposed to the mental approaches, have recently received a considerable attention in the area of agent communication. They exploit observable social commitments to develop a verifiable formal semantics through which communication protocols can be specified. Developing and implementing algorithmic model checking for social commitments have been recently addressed. However, model checking social commitments in the presence of uncertainty is yet to be investigated.In this paper, we propose a model checking technique for verifying social commitments in uncertain settings. Social commitments are specified in a modal logical language called Probabilistic Computation Tree Logic of Commitments (PCTLC). The modal logic PCTLC extends PCTL, the probabilistic extension of CTL, with modalities for commitments and their fulfillments. The proposed verification method is a reduction-based model checking technique to the model checking of PCTL. The technique is based upon a set of reduction rules that translate PCTLC formulae to PCTL formulae to take benefit of existing model checkers such as PRISM. Proofs that confirm the soundness of the reduction technique are presented. We also present rules that transform our new version of interpreted systems into models of Markov Decision Processes (MDPs) to be suitable for the PRISM tool. We implemented our approach on top of the PRISM model checker and verified some given properties for the Oblivious Transfer Protocol from the cryptography domain. Our simulation demonstrates the effectiveness of our approach in verifying and model checking social commitments in the presence of uncertainty. We believe that the proposed formal verification technique will advance the literature of social commitments in such a way that not only representing social commitments in uncertain settings is doable, but also verifying them in such settings becomes achievable.     

Parallel probabilistic model checking on general purpose graphics processors

We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). Our improvements target the numerical components of the traditional sequential algorithms. In particular, we capitalize on the fact that in most of them operations like matrix–vector multiplication and solving systems of linear equations are the main complexity bottlenecks. Since linear algebraic operations can be implemented very efficiently on GPGPUs, the new parallel algorithms show considerable runtime improvements compared to their counterparts on standard architectures. We implemented our parallel algorithms on top of the probabilistic model checker PRISM. The prototype implementation was evaluated on several case studies in which we observed significant speedup over the standard CPU implementation of the tool.     

Word level bitwidth reduction for unbounded hardware model checking

In this paper we present a word-level model checking method that attempts to speed up safety property checking of industrial netlists. Our aim is to construct an algorithm that allows us to check both bounded and unbounded properties using standard bit-level model checking methods as back-end decision procedures, while incurring minimum runtime penalties for designs that are unsuited to our analysis. We do this by combining modifications of several previously known techniques into a static abstraction algorithm which is guaranteed to produce bit-level netlists that are as small or smaller than the original bitblasted designs. We evaluate our algorithm on several challenging hardware components.     

Performance Evaluation of a Real-time Simulation Architecture using Probabilistic Model Checking

The goal of this paper is to show how to use probabilistic model checking techniques in order to achieve quantitative performance evaluation of a real-time distributed simulation. A simulation based on the High Level Architecture (HLA) is modelled as a stochastic process, a Continuous Time Markov Chain (CTMC), using the stochastic algebra PEPA. Next a property representing a performance constraint is evaluated applying Continuous Stochastic Logic CSL formula on the CTMC model using the probabilistic model checker PRISM. Finally a first experiment is made to compare the model with a real case.     

面向复杂随机系统的启发式统计模型检测方法 *

统计模型检测是一种高效的验证技术,常用于复杂的随机系统验证,如分布式算法等。而在超长路径上对性质进行验证时,其验证效率会急剧降低。为解决这个问题,这里提出一种启发式的统计模型检测算法。在对路径进行验证时,我们会查找帮助剪枝的最短前缀。并在后续抽样时,利用前缀信息直接判定路径是否满足给定性质,避免进入费时的路径验证阶段。在与PRISM的比较中,它的路径验证次数相对更少且平均抽样路径长度更短。因此使统计模型检测技术可应用于超长路径上的性质验证。     

Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal

Electrocardiography (ECG) is a heart signal wave that is recorded using medical sensors, which are normally attached to the human body by the heart. ECG waves have repetitive patterns that can be efficiently used in the diagnosis of heart problems as they carry several characteristics of heart operation. Traditionally, the analysis of ECG waves is done using informal techniques, like simulation, which is in-exhaustive and thus the analysis results may lead to ambiguities and life threatening scenarios in extreme cases. In order to overcome such problems, we propose to analyze ECG heart signals using probabilistic model checking, which is a formal methods based quantitative analysis approach. This work presents the formal probabilistic analysis of ECG signal abnormalities where the likelihood of abnormal patterns is studied and analyzed using the PRISM model checker.     

LTL概率模型检验工具的实现与优化

概率模型检验建立在非概率模型检验技术的基础上,不仅能够对系统进行定性的验证,还能够定量判断系统满足相关性质的概率,具有广泛的适用性。LTL概率模型检验算法的复杂度较高,达到双重指数级别,现有的工具如PRISM与MRMC均不支持对LTL性质的验证。针对这个问题,通过对原有的LTL概率模型检验算法进行优化,实现了一个高效的LTL概率模型检验工具。通过对比实验验证了该工具的有效性。     

Quantitative Analysis With the Probabilistic Model Checker PRISM

Probabilistic model checking is a formal verification technique for establishing the correctness, performance and reliability of systems which exhibit stochastic behaviour. As in conventional verification, a precise mathematical model of a real-life system is constructed first, and, given formal specifications of one or more properties of this system, an analysis of these properties is performed. The exploration of the system model is exhaustive and involves a combination of graph-theoretic algorithms and numerical methods. In this paper, we give a brief overview of the probabilistic model checker PRISM (www.cs.bham.ac.uk/~dxp/prism) implemented at the University of Birmingham. PRISM supports a range of probabilistic models and specification languages based on temporal logic, and has been recently extended with costs and rewards. We describe our experience with using PRISM to analyse a number of case studies from a wide range of application domains. We demonstrate the usefulness of probabilistic model checking techniques in detecting flaws and unusual trends, focusing mainly on the quantitative analysis of a range of best, worst and average-case system characteristics.     

基于SMT的PTACTL限界模型检测方法

概率时间自动机是在时间自动机的基础上加上各个状态迁移的概率以后形成的一种扩展的时间自动机,能用来对基于时间的随机协议、容错系统等进行建模,具有很强的实用性。本文针对概率时间自动机给出一种基于SMT的限界模型检测方法来验证该模型下的PTACTL性质,该方法由基于SMT的限界模型检测算法演变而来,通过将迁移时间和迁移概率融入ACTL性质中,改变模型的编码以及待验证性质的编码方式来实现对性质的验证。通过2个实例说明检测过程的有效性和高效性。     

Model checking action system refinements

Action systems provide a formal approach to modelling parallel and reactive systems. They have a well established theory of refinement supported by simulation-based proof rules. This paper introduces an automatic approach for verifying action system refinements utilising standard CTL model checking. To do this, we encode each of the simulation conditions as a simulation machine, a Kripke structure on which the proof obligation can be discharged by checking that an associated CTL property holds. This procedure transforms each simulation condition into a model checking problem. Each simulation condition can then be model checked in isolation, or, if desired, together with the other simulation conditions by combining the simulation machines and the CTL properties.     

Scientific Theories of Computational Systems in Model Checking

Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings between these scientific theories and computational systems in their scope are analyzed in terms of suitable specializations of the notions of model of experiment and model of data. Furthermore, the extensively mechanized character of model-based reasoning in model checking is highlighted by a comparison with proof procedures adopted by other formal methods in computer science. Finally, potential epistemic benefits flowing from the application of model checking in other areas of scientific inquiry are emphasized in the context of computer simulation studies of biological information processing.     

Monte Carlo solution of structural dynamics

The recent advent of high speed digital computers has made it not only possible but also highly practical to apply the Monte Carlo techniques to a large variety of engineering problems. In this paper a technique of digital simulation of multivariate and/or multidimensional Gaussian random processes (homogeneous or nonhomogeneous) which can represent physical processes germane to structural engineering is presented. The paper also describes a method of digital simulation of envelope functions. Such simulations are accomplished in terms of a sum of cosine functions with random phase angles and used as the basic tool in a general Monte Carlo method of solution of a wide class of problems in structural engineering. Most important problems for which the method is found extremely useful includes (a) numerical analysis of dynamic response of nonlinear structures to random excitations, (b) time domain analysis of linear structures under random excitations performed for the purpose of obtaining a kind of information, such as first excursion probability and time history of a sample function, that is not obtainable from the standard frequency domain analysis, (c) numerical solution of structural problems involving randomly nonhomogeneous material property such as wave propagation through random medium, and (d) dynamic analysis of extremely complex systems such as those involving structure-fluid interaction. Numerical examples of some of these problems are presented.     

基于模型检测的系统生存性分析

提出一种采用模型检测进行系统生存性分析的形式化方法。给出系统所处环境及主要提供的服务,引入灾难和错误等因素,建立系统生存性模型。通过描述系统的可生存能力,确定其生存性需求并转换为相应的逻辑表示。以电话接入网络为例,利用PRISM对系统进行建模及验证,结果表明,该形式化方法可以规范并简化生存性分析过程。     

Knowledge as Strategic Ability

The ultimate goal of our research is to develop techniques for model checking knowledge properties of multi-agent systems. ATEL, an extension of the Alternating-time Temporal Logic of Alur et al, is a logic for specifying epistemic and strategic properties of such systems. We present a technique for reducing the ATEL model checking problem to one of model checking in ATL, whereby epistemic relations are explicitly encoded in ATL models as as dynamic transitions. The techniques is illustrated by means of a knowledge game, which is used as a running example throughout the paper.     

Statistical probabilistic model checking with a focus on time-bounded properties

Probabilistic verification of continuous-time stochastic processes has received increasing attention in the model-checking community in the past five years, with a clear focus on developing numerical solution methods for model checking of continuous-time Markov chains. Numerical techniques tend to scale poorly with an increase in the size of the model (the “state space explosion problem”), however, and are feasible only for restricted classes of stochastic discrete-event systems. We present a statistical approach to probabilistic model checking, employing hypothesis testing and discrete-event simulation. Since we rely on statistical hypothesis testing, we cannot guarantee that the verification result is correct, but we can at least bound the probability of generating an incorrect answer to a verification problem.     

Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Multi-Agent Systems (MASs) have long been modeled through knowledge and social commitments independently. In this paper, we present a new method that merges the two concepts to model and verify MASs in the presence of uncertainty. To express knowledge and social commitments simultaneously in uncertain settings, we define a new multi-modal logic called Probabilistic Computation Tree Logic of Knowledge and Commitments (PCTL^kc in short) which combines two existing probabilistic logics namely, probabilistic logic of knowledge PCTLK and probabilistic logic of commitments PCTLC. To model stochastic MASs, we present a new version of interpreted systems that captures the probabilistic behavior and accounts for the communication between interacting components. Then, we introduce a new probabilistic model checking procedure to check the compliance of target systems against some desirable properties written in PCTL^kc and report the obtained verification results. Our proposed model checking technique is reduction-based and consists in transforming the problem of model checking PCTL^kc into the problem of model checking a well established logic, namely PCTL. So doing provides us with the privilege of re-using the PRISM model checker to implement the proposed model checking approach. Finally, we demonstrate the effectiveness of our approach by presenting a real case study. This framework can be considered as a step forward towards closing the gap of capturing interactions between knowledge and social commitments in stochastic agent-based systems.