Combining finite learning automata with GSAT for the satisfiability problem

A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT used with random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing randomized and structured problems from various domains.     

Generating effective test cases based on satisfiability modulo theory solvers for service‐oriented workflow applications

Web Service Business Process Execution Language (WS‐BPEL) is one of the most popular service‐oriented workflow applications. The unique features (e.g. dead path elimination semantics and correlation mechanism) of WS‐BPEL applications have raised enormous problems to its test case generation, especially in unit testing. Existing studies mainly assume that each path in the control flow graphs that correspond to WS‐BPEL applications is feasible, which always yields imprecise test cases or complicates testing results. The current study tackles this problem based on satisfiability modulo theory solvers. First, a new coverage criterion is proposed to measure the quality of test sets for testing WS‐BPEL applications. Second, decomposition algorithms are presented to obtain test paths that meet the proposed coverage criterion. Finally, this paper symbolically encodes each test path with several constraints by capturing the unique features of WS‐BPEL. These constraints are solved and the test cases (test paths and test data) are obtained with the help of satisfiability modulo theory solvers to test WS‐BPEL applications effectively. Experiments are conducted using our approach and other typical approaches (e.g. message‐sequence generation‐based approach and concurrent path analysis approach) with 10 WS‐BPEL applications. Experimental results demonstrate that the test cases generated by our approach can avoid instantiating idle instance and expose more faults. Copyright © 2015 John Wiley & Sons, Ltd.     

最优并行算法评价标准的研究

加速比是判断一个并行虎法是否最优的依据，但播送类问题是针对并行机提出的，不存在串行算法，加速比标准对之无能为力，通过对几种不同并行计算模型上播送算法的研究，文中提出了一个不依赖于上体模型的一般化的评价标准ｍｉｎＣ＾２用以判断播送算法是否最优，为这类问题的进一步工辟了新的思路。     

The giving tree: constructing trees for efficient offline and online multi-robot coverage

This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multi-robot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper focuses on the challenge of constructing a coverage spanning tree for both online and offline coverage that minimizes the time to complete coverage. Our general approach involves building a spanning tree by growing sub-trees from the initial location of the robots. This paper first describes a polynomial time tree-construction algorithm for offline coverage. The use of this algorithm is shown by extensive simulations to significantly improve the coverage time of the terrain even when used as a basis for a simple, inefficient, coverage algorithm. Second, this paper provides an algorithm for online coverage of a finite terrain based on spanning-trees, that is complete and guarantees linear time coverage with no redundancy in the coverage. In addition, the solutions proposed by this paper guarantee robustness to failing robots: the offline trees are used as base for robust multi-robot coverage algorithms, and the online algorithm is proven to be robust.     

一种改进的两两组合测试用例生成方法

本文针对多因素二水平系统提出:将所有的测试数据表示为一棵二叉解空间树,用回溯法对二叉解空间树进行路径搜索来生成测试用例,然后使用贪心算法生成补充的测试用例,以满足两两组合覆盖标准。使用这种方法生成的测试数据集具有很高的覆盖率。     

构件组装实时系统行为相容性测试用例产生

虽然构件技术在软件开发过程中得到了越来越广泛的应用,但是实时系统是一类设计、实现和验证工作都相当复杂的系统,其构件化远比普通软件复杂,组装仍有许多困难。分析了常见的组装相容性错误,提出了一种实时系统的构件组装行为相容性测试用例产生方法。首先对时间自动机进行扩展,给出了描述实时构件的模型;然后定义了相容性覆盖标准,并把构件行为相容性测试用例生成转化为可被模型检验支持的可达性分析,同时给出了算法;最后用一个实例展示了该方法的具体使用。     

Path-oriented bounded reachability analysis of composed linear hybrid systems

The existing techniques for reachability analysis of linear hybrid systems do not scale well to the problem size of practical interest. The performance of existing techniques is even worse for reachability analysis of a composition of several linear hybrid automata. In this paper, we present an efficient path-oriented approach to bounded reachability analysis of composed systems modeled by linear hybrid automata with synchronization events. It is suitable for analyzing systems with many components by selecting critical paths, while this task was quite insurmountable before because of the state explosion problem. This group of paths will be transformed to a group of linear constraints, which can be solved by a linear programming solver efficiently. This approach of symbolic execution of paths allows design engineers to check important paths, and accordingly increase the faith in the correctness of the system. This approach is implemented into a prototype tool Bounded reAchability CHecker (BACH). The experimental data show that both the path length and the number of participant automata in a system checked using BACH can scale up greatly to satisfy practical requirements.     

Water wave optimization for combinatorial optimization: Design strategies and applications

Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few studies have been devoted to reducing this burden. This paper proposes a systematic approach that consists of a set of basic steps and strategies for adapting water wave optimization (WWO), a simple and generic metaheuristic, to concrete heuristic algorithms for different COPs. Taking advantages of the generic algorithmic framework, designers can only focus on adapting the prorogation operator and the wavelength calculation method according to the combinatorial properties of the given problem, and thus easily derive efficient problem-solving algorithms. We illustrate and test our approach on the flow-shop scheduling problem (FSP), the single-objective multidimensional knapsack problem (MKP), and the multi-objective MKP, and then present an application to a machine utilization optimization problem for a large manufacturing enterprise. The results demonstrate that our approach can derive concrete algorithms that are competitive to the state-of-the-arts. Our approach also provides insights into the adaptation of other metaheuristics and the development of new metaheuristics for COPs.     

A Cost-Regular Based Hybrid Column Generation Approach

Constraint Programming (CP) offers a rich modeling language of constraints embedding efficient algorithms to handle complex and heterogeneous combinatorial problems. To solve hard combinatorial optimization problems using CP alone or hybrid CP-ILP decomposition methods, costs also have to be taken into account within the propagation process. Optimization constraints, with their cost-based filtering algorithms, aim to apply inference based on optimality rather than feasibility. This paper introduces a new optimization constraint, cost-regular. Its filtering algorithm is based on the computation of shortest and longest paths in a layered directed graph. The support information is also used to guide the search for solutions. We believe this constraint to be particularly useful in modeling and solving Column Generation subproblems and evaluate its behaviour on complex Employee Timetabling Problems through a flexible CP-based column generation approach. Computational results on generated benchmark sets and on a complex real-world instance are given.A preliminary version of this paper appeared as [7]. This research was supported by the Mathematics of Information Technology and Complex Systems (MITACS) Internship program in association with Omega Optimisation Inc. (CA).     

Models and methods for solving the problem of network vulnerability

The problem of analysis of the vulnerability of single-commodity and multi-commodity networks taking into account complete breakdown of one or several arcs is considered. The guaranteed estimate of the loss of users of this network is chosen as the criterion of the network performance. A number of formal statements of the problem are proposed for vulnerability analysis. These statements of the problem are studied by flow, graph theoretical, probabilistic, and other methods. The cases when these statements of the problem are polynomially solvable are specified, and the corresponding algorithms are given. In the other cases, it is proposed to use approximation, combinatorial, and other methods.     

On properties of predictors derived with a two-step bootstrap model averaging approach—A simulation study in the linear regression model

In many applications of model selection there is a large number of explanatory variables and thus a large set of candidate models. Selecting one single model for further inference ignores model selection uncertainty. Often several models fit the data equally well. However, these models may differ in terms of the variables included and might lead to different predictions. To account for model selection uncertainty, model averaging procedures have been proposed. Recently, an extended two-step bootstrap model averaging approach has been proposed. The first step of this approach is a screening step. It aims to eliminate variables with negligible effect on the outcome. In the second step the remaining variables are considered in bootstrap model averaging. A large simulation study is performed to compare the MSE and coverage rate of models derived with bootstrap model averaging, the full model, backward elimination using Akaike and Bayes information criterion and the model with the highest selection probability in bootstrap samples. In a data example, these approaches are also compared with Bayesian model averaging. Finally, some recommendations for the development of predictive models are given.     

State generation and automated class testing

The maturity of object‐oriented methods has led to the wide availability of container classes: classes that encapsulate classical data structures and algorithms. Container classes are included in the C++ and Java standard libraries, and in many proprietary libraries. The wide availability and use of these classes makes reliability important, and testing plays a central role in achieving that reliability. The large number of cases necessary for thorough testing of container classes makes automated testing essential. This paper presents a novel approach for automated testing of container classes based on combinatorial algorithms for state generation. The approach is illustrated with black‐box and white‐box test drivers for a class implemented with the red–black tree data structure, used widely in industry and, in particular, in the C++ Standard Template Library. The white‐box driver is based on a new algorithm for red–black tree generation. The drivers are evaluated experimentally, providing quantitative measures of their effectiveness in terms of block and path coverage. The results clearly show that the approach is affordable in terms of development cost and execution time, and effective with respect to coverage achieved. The results also provide insight into the relative advantages of black‐box and white‐box drivers, and into the difficult problem of infeasible paths. Copyright © 2000 John Wiley & Sons, Ltd.     

基于解空间树的组合测试数据生成

在组合覆盖测试模型的基础上提出：将所有的可用测试数据表示为一棵解空间树,利用回溯法对解空间树进行路径搜索来生成测试数据,然后使用贪心算法补充生成测试数据,以满足两两组合覆盖标准.并且实现了基于该方法的测试数据生成工具,所生成的测试数据集与同类工具相比具有一定的特点和优势.     

Intelligent Coverage Path Planning for Agricultural Robots and Autonomous Machines on Three-Dimensional Terrain

Field operations should be done in a manner that minimizes time and travels over the field surface. Automated and intelligent path planning can help to find the best coverage path so that costs of various field operations can be minimized. The algorithms for generating an optimized field coverage pattern for a given 2D field has been investigated and reported. However, a great proportion of farms have rolling terrains, which have a considerable influence on the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations and provide more precise navigation. Supplementary to that, energy consumption models were invoked taking into account terrain inclinations in order to provide the optimal driving direction for traversing the parallel field-work tracks and the optimal sequence for handling these tracks under the criterion of minimizing direct energy requirements. The reduced energy requirements and consequently the reduced emissions of atmospheric pollutants, e.g. CO₂ and NO, are of major concern due to their contribution to the greenhouse effect. Based on the results from two case study fields, it was shown that the reduction in the energy requirements when the driving angle is optimized by taking into account the 3D field terrain was 6.5 % as an average for all the examined scenarios compared to the case when the applied driving angle is optimized assuming even field terrain. Additional reduction is achieved when sequence of field tracks is optimized by taking into account inclinations for driving up and down steep hills.     

基于组织进化粒子群优化的测试用例自动生成

针对组合测试用例生成问题的具体特点,结合组织进化思想及粒子群优化算法,设计了适合问题求解的编码方式及操作算子等,提出了一种基于组织进化粒子群优化的测试用例自动生成算法。该方法用于选择当前局部优化覆盖的测试用例,在此基础上构建满足两两覆盖的测试用例集。仿真实验表明,该方法能有效地降低测试用例数目。     

Efficient synthesis of production schedules by optimization of timed automata

For industrial systems with multiple products or alternative production paths, the time-optimal assignment of production steps to available units is often economically indispensable. Among the several methods proposed for solving such job-shop scheduling problems, techniques based on reachability analysis for timed automata (TA) have gained attention recently. It was proposed recently to integrate techniques known from mixed-integer programming (MIP) into reachability analysis for TA to establish an efficient pruning of the search graph. This paper further improves the efficiency of the algorithm by excluding suboptimal or redundant solutions from the search space. For several benchmark problems, it is shown that the proposed approach can produce better schedules in a given computation time as are obtained by applying pure MIP to large problem instances.     

组合测试用例生成技术

组合测试是一种科学有效的软件测试方法,该方法旨在使用较少的测试用例有效地检测软件系统中各个因素以及它们之间的相互作用对系统产生的影响,实践证明其具有较高的错误检测能力。当前组合测试研究的热点之一是组合测试用例生成问题,即如何针对具体待测软件,在满足给定组合覆盖要求的前提下,生成规模尽可能小的测试用例集,以便在保证错误检测能力的前提下尽可能降低测试成本。从N维组合覆盖和变力度组合覆盖等两类不同的组合覆盖标准出发,简要介绍了迄今为止人们在组合测试用例生成领域所取得的研究成果,对现有的组合用例生成方法进行了分类和总结。此外,还对优先级、组合约束、错误定位等条件和应用场景下的组合测试用例生成技术进行了介绍。最后,分析了现有成果中存在的问题,并对该领域未来的研究方向进行了分析和讨论。     

A general criterion and an algorithmic framework for learning in multi-agent systems

We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also be learning (using the same or other learning algorithms). This new criterion takes in as a parameter the class of opponents. We then provide a modular approach for achieving effective agent-centric learning; the approach consists of a number of basic algorithmic building blocks, which can be instantiated and composed differently depending on the environment setting (for example, 2- versus n-player games) as well as the target class of opponents. We then provide several specific instances of the approach: an algorithm for stationary opponents, and two algorithms for adaptive opponents with bounded memory, one algorithm for the n-player case and another optimized for the 2-player case. We prove our algorithms correct with respect to the formal criterion, and furthermore show the algorithms to be experimentally effective via comprehensive computer testing.     

The AETG system: an approach to testing based on combinatorialdesign

This paper describes a new approach to testing that uses combinatorial designs to generate tests that cover the pairwise, triple, or n-way combinations of a system's test parameters. These are the parameters that determine the system's test scenarios. Examples are system configuration parameters, user inputs and other external events. We implemented this new method in the AETG system. The AETG system uses new combinatorial algorithms to generate test sets that cover all valid n-way parameter combinations. The size of an AETG test set grows logarithmically in the number of test parameters. This allows testers to define test models with dozens of parameters. The AETG system is used in a variety of applications for unit, system, and interoperability testing. It has generated both high-level test plans and detailed test cases. In several applications, it greatly reduced the cost of test plan development     

A comparison of collapsed Bayesian methods for probabilistic finite automata

This paper describes several collapsed Bayesian methods, which work by first marginalizing out transition probabilities, for inferring several kinds of probabilistic finite automata. The methods include collapsed Gibbs sampling (CGS) and collapsed variational Bayes, as well as two new methods. Their targets range over general probabilistic finite automata, hidden Markov models, probabilistic deterministic finite automata, and variable-length grams. We implement and compare these algorithms over the data sets from the Probabilistic Automata Learning Competition (PAutomaC), which are generated by various types of automata. We report that the CGS-based algorithm designed to target general probabilistic finite automata performed the best for any types of data.