No time for compliance

In the past few years several business process compliance framework based on temporal logic have been proposed. In this paper we investigate whether the use of temporal logic is suitable for the task at hand: namely to check whether the specifications of a business process are compatible with the formalisation of the norms regulating the business process. We provide an example inspired by real life norms where the use of linear temporal logic produces a result that is not compatible with the legal understanding of the norms in the example.

Permissions in Deontic Event-Calculus

Permissions are special case of deontic effects and play important role compliance. Essentially they are used to determine the obligations or prohibitions to contrary. A formal language e.g., temporal logic, event-calculus et., not able to represent permissions is doomed to be unable to represent most of the real-life legal norms. In this paper we address this issue and extend deontic-event-calculus (DEC) with new predicates for modelling permissions enabling it to elegantly capture the intuition of real-life cases of permissions.

Automata and logics over finitely varying functions

We extend some of the classical connections between automata and logic due to Büchi (1960) [5] and McNaughton and Papert (1971) [12] to languages of finitely varying functions or “signals”. In particular, we introduce a natural class of automata for generating finitely varying functions called View the MathML source’s, and show that it coincides in terms of language definability with a natural monadic second-order logic interpreted over finitely varying functions Rabinovich (2002) [15]. We also identify a “counter-free” subclass of View the MathML source’s which characterise the first-order definable languages of finitely varying functions. Our proofs mainly factor through the classical results for word languages. These results have applications in automata characterisations for continuously interpreted real-time logics like Metric Temporal Logic (MTL) Chevalier et al. (2006, 2007) [6] and [7].

Formal methods for control synthesis in partially observed environments : application to autonomous robotic manipulation

Modern robots are increasingly expected to function in uncertain and dynamically challenging environments, often in proximity with humans. In addition, wide scale adoption of robots requires on-the-fly adaptability of software for diverse application. These requirements strongly suggest the need to adopt formal representations of high level goals and safety specifications, especially as temporal logic formulas. This approach allows for the use of formal verification techniques for controller synthesis that can give guarantees for safety and performance. Robots operating in unstructured environments also face limited sensing capability. Correctly inferring a robot's progress toward high level goal can be challenging.

This thesis develops new algorithms for synthesizing discrete controllers in partially known environments under specifications represented as linear temporal logic (LTL) formulas. It is inspired by recent developments in finite abstraction techniques for hybrid systems and motion planning problems. The robot and its environment is assumed to have a finite abstraction as a Partially Observable Markov Decision Process (POMDP), which is a powerful model class capable of representing a wide variety of problems. However, synthesizing controllers that satisfy LTL goals over POMDPs is a challenging problem which has received only limited attention.

This thesis proposes tractable, approximate algorithms for the control synthesis problem using Finite State Controllers (FSCs). The use of FSCs to control finite POMDPs allows for the closed system to be analyzed as finite global Markov chain. The thesis explicitly shows how transient and steady state behavior of the global Markov chains can be related to two different criteria with respect to satisfaction of LTL formulas. First, the maximization of the probability of LTL satisfaction is related to an optimization problem over a parametrization of the FSC. Analytic computation of gradients are derived which allows the use of first order optimization techniques.

The second criterion encourages rapid and frequent visits to a restricted set of states over infinite executions. It is formulated as a constrained optimization problem with a discounted long term reward objective by the novel utilization of a fundamental equation for Markov chains - the Poisson equation. A new constrained policy iteration technique is proposed to solve the resulting dynamic program, which also provides a way to escape local maxima.

The algorithms proposed in the thesis are applied to the task planning and execution challenges faced during the DARPA Autonomous Robotic Manipulation - Software challenge.

Efficient methods for stochastic optimal control

The Hamilton Jacobi Bellman (HJB) equation is central to stochastic optimal control (SOC) theory, yielding the optimal solution to general problems specified by known dynamics and a specified cost functional. Given the assumption of quadratic cost on the control input, it is well known that the HJB reduces to a particular partial differential equation (PDE). While powerful, this reduction is not commonly used as the PDE is of second order, is nonlinear, and examples exist where the problem may not have a solution in a classical sense. Furthermore, each state of the system appears as another dimension of the PDE, giving rise to the curse of dimensionality. Since the number of degrees of freedom required to solve the optimal control problem grows exponentially with dimension, the problem becomes intractable for systems with all but modest dimension.

In the last decade researchers have found that under certain, fairly non-restrictive structural assumptions, the HJB may be transformed into a linear PDE, with an interesting analogue in the discretized domain of Markov Decision Processes (MDP). The work presented in this thesis uses the linearity of this particular form of the HJB PDE to push the computational boundaries of stochastic optimal control.

This is done by crafting together previously disjoint lines of research in computation. The first of these is the use of Sum of Squares (SOS) techniques for synthesis of control policies. A candidate polynomial with variable coefficients is proposed as the solution to the stochastic optimal control problem. An SOS relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap. The resulting approximate solutions are shown to be guaranteed over- and under-approximations for the optimal value function. It is shown that these results extend to arbitrary parabolic and elliptic PDEs, yielding a novel method for Uncertainty Quantification (UQ) of systems governed by partial differential constraints. Domain decomposition techniques are also made available, allowing for such problems to be solved via parallelization and low-order polynomials.

The optimization-based SOS technique is then contrasted with the Separated Representation (SR) approach from the applied mathematics community. The technique allows for systems of equations to be solved through a low-rank decomposition that results in algorithms that scale linearly with dimensionality. Its application in stochastic optimal control allows for previously uncomputable problems to be solved quickly, scaling to such complex systems as the Quadcopter and VTOL aircraft. This technique may be combined with the SOS approach, yielding not only a numerical technique, but also an analytical one that allows for entirely new classes of systems to be studied and for stability properties to be guaranteed.

The analysis of the linear HJB is completed by the study of its implications in application. It is shown that the HJB and a popular technique in robotics, the use of navigation functions, sit on opposite ends of a spectrum of optimization problems, upon which tradeoffs may be made in problem complexity. Analytical solutions to the HJB in these settings are available in simplified domains, yielding guidance towards optimality for approximation schemes. Finally, the use of HJB equations in temporal multi-task planning problems is investigated. It is demonstrated that such problems are reducible to a sequence of SOC problems linked via boundary conditions. The linearity of the PDE allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to satisfy a complex temporal logic specification.

一种基于模型的并发系统测试目的描述

提出了带约束事件的时序逻辑TLCE,用于描述系统运行中输入/输出事件之间的时序关系以及对事件参数的数据相关性约束.阐述了一种基于模型的并发系统测试框架,采用TLCE描述测试目的以引导测试用例生成.缓存一致性协议和会议协议的实例研究中所生成的测试用例集显著优于随机测试用例集.这说明了TLCE作为测试目的描述的有效性.

基于pCTL的循环优化测试用例自动生成方法的研究与实现

编译优化是现代编译器的重要功能，编译优化测试对保障现代编译器质量有着重要作用。编译优化测试需要编写大量的测试用例程序作为输入，手工完成十分费时费力，因此，有必要研究编译优化测试用例的自动生成方法。 针对编译优化测试，有研究者提出并实现了一种基于分支时序逻辑的测试用例自动生成方法COT。该方法可以有效地生成标量优化测试用例程序，但该方法没有考虑程序中的数组和指针，对优化特征的描述也不能区分循环迭代中的语句实例，更不能刻画语句实例间的数据依赖关系，然而这些是刻画循环优化所必需的，因此，COT不适用于循环优化测试用例的自动生成。 本文针对COT方法的缺陷，首先提出了基于参数化分支时序逻辑pCTL (parameterized computation temporal logic)的循环优化描述方法，通过参数化COT的优化描述体系中的语句谓词、变量引用谓词和变量定义谓词实现了对循环迭代中的语句实例及语句实例间的数据依赖关系的描述。在此基础上，本文提出了基于pCTL 描述的循环优化测试用例自动生成方法COT2。该方法根据pCTL公式构造初始的关键节点控制图，再按照公式语义执行公式处理和虚边替换，得到完整的控制流图，最后计算数组引用下标，生成循环优化测试用例程序。 本文实现了COT2的系统原型，并用循环优化模块的覆盖率指标评价生成的测试用例的质量，实验结果表明该方法对循环优化具有针对性，是一种行之有效的方法。本文还用生成的测试用例程序对GCC各版本的循环优化模块进行了测试，并分析了错误发现数与各版本稳定性之间的关系，进一步验证了本文方法的有效性。

面向嵌入式实时软件的需求规约语言及检测方法

针对嵌入式实时软件需求规约及其检测问题,提出了基于层次并发有穷状态机的可合成的图形化建模语言RTRSM*(real-time requirements specification model*),利用转换有效期和事件预定机制来描述时间限制,能够较好地支持系统交互性和实时性的建模.为弥补RTRSM*作为操作性规约语言不便于性质描述的问题,提出了命题时序逻辑RITL(real-time interval temporal logic).该语言以时间状态序列为语义模型,具有基于区间和时间点的量化时间属性描述功能,能自然、全面地描述RTRSM*模型性质.介绍并讨论了基于两种语言的规约检测方法和技术,主要包括系统状态空间有穷的RTRSM*模型状态可达图的相关问题和规约的模拟执行.

一种编译优化测试用例自动生成方法的设计与实现

编译器的质量保证对提高软件产品的质量有着重要作用,对编译优化的测试是其中的核心部分.对编译优化的测试需要大量的测试用例程序.要构造这些测试用例,使用传统手工构造方法面临着效率低的问题,而基于文法的构造方法则针对性不足.从对优化的形式化描述出发来自动构造测试用例能克服这些缺点.本文设计并实现了一种基于形式化描述的编译优化测试用例程序生成方法.该方法基于编译优化的时序逻辑描述构造关键顶点控制流图,逐步转换为控制流图并得到用例程序.针对GCC(版本4.1.1)进行的覆盖率测试实验表明,该方法可以生成具有较高针对性的测试用例,并达到相当的覆盖程度.

基于时序逻辑证明编译优化程序变换的保义性

基于时序逻辑CTL(computation tree logic)的一种扩展CTL-FV对优化编译中的语句交换和变量替换这两种常见变换的保义性条件给出了形式刻画,采用含条件重写规则定义了保义语句交换Texch和保义变量替换Tsub,并基于一种归纳证明框架对它们的保义性进行了证明.此外,基于变换Texch对程序基本块内保依赖语句重排的保义性也给出了一种构造性的证明.

基于时序逻辑的编译优化测试用例生成方法

提出一种基于时序逻辑公式的关键节点控制图生成方法,生成的测试用例针对性强,容易扩展;并以该方法改进了一种编译优化自动化测试工具,在很大程度上消除了其测试冗余,提高了测试效率