Case‐based approach for supporting strategy decision making

The practice of strategy decision making proves that when the management board is strongly limited in its capacity to take rational actions, specifically in the context of great decision complexity and uncertainty, it is considered good practice to refer to experience through reasoning by analogy. In this paper, we would like to concentrate on supporting strategic decisions in small and medium enterprises (SMEs). The complexity of analogy‐based reasoning has its roots in an attempt to solve new problems based on past cases from a different domain, while we will focus on case‐based approach for a single domain. Additionally, we have chosen case‐based reasoning as a suitable decision‐making paradigm because it is corresponds to managers’ decision‐making behaviour. We present the STRATEGOS case‐based reasoning system for supporting strategic decision making by SMEs management boards and then the system evaluation by the dozens of chief executive officers (CEOs) from SMEs is presented. The results of the survey are promising and show the remarkable correspondence of the proposed solution with expectations and strategic behaviour of CEOs.     

基于案例分析金字塔模型的主动运维案例体系建设

针对传统运维案例在经验共享和处置效率方面的不足及运维领域同类事件重复发生概率高、处置差异大的情况,提出一种案例分析金字塔模型,使用基于向量化解析和知识分层提取的素材库构建方法,实现由生产运维操作标准库、生产运行异常事件库到案例素材库再到案例教学库的自动生成转变,并以金字塔模型为基础,通过引入改进的趋势预测分析方法、案例推理"5R"模型和"4S"案例教学模式完善预警预测、辅助决策、案例教学相关应用,建立主动运维案例体系。实践结果表明,通过整个体系的推行,改变了企业信息系统运维被动应付的局面,同类事件占比逐年降低,事件响应时间和处置时间明显缩短,运维服务质量大幅提升。     

Construction-specific spatial information reasoning in Building Information Models

In recent years, there have been significant advances in modeling technology for object-oriented building products. However, the building models are still lacking of providing construction-specific spatial information required for construction planning. Consequently, construction planners visually analyze building product models and derive geometric characteristics such as bounded spaces and exterior perimeter to develop detailed construction plans. Such a process presents fragmented information flows, from building product information to construction planning, that rely on subjective decisions of construction planners. In order to overcome these drawbacks, this research proposes a geometric reasoning system that analyzes geometric information in building designs, derives the construction-specific spatial information, and uses the information to assist in construction planning. The scope of presented work includes detecting work packages formed by faces during construction, such as large work faces and bounded spaces, and using information in the work packages directly to support planning of selected indoor construction activities. The main features of the proposed system named Construction Spatial Information Reasoner (CSIR) include a set of relationship acquisition algorithms, building component relationship data structure, and interpretation of the relationship to support detailed construction activity planning. The relationship acquisition algorithms identify adjacency between building components that is stored in the relational data structure. Then, acquired adjacency relationships are transformed into a set of graphs that represent work packages. To implement the proposed approach, CSIR utilized a commercially-available Building Information Modeling (BIM) platform and the algorithms were imbedded to the BIM platform. For validation, CSIR was tested on a real commercial building. For interior ceiling grid installation activities, CSIR successfully detected existing work packages and analyzed the spatial characteristics impacting construction productivity. The major contribution of the presented research would be to enable a realistic analysis of building geometric condition that is not possible in current BIM and a seamless information flow from building product information to construction process plans. These can potentially reduce current manual and error-prone construction planning processes. Limitations and future research suggestions are also presented.     

基于Value双向近似推理的系统决策方法

先提出Vague值间的一种新的相似度,并基于有序加权聚类(OWA)算子给出Vague集间的新的加权相似测度,其中的权重是由决策者根据实际决策需求来确定的,具有一定的弹性;另外通过Vague输入事实与Vague决策规则的前件(或后件)之间的相似匹配度,得到一个简单可行的双向近似推理方法,这可为复杂环境下的系统决策(如工业检测和医疗诊断等领域问题)提供有效的支持工具。     

系统L在非均匀概率空间下命题的真度理论

将经典二值命题逻辑L中公式的真度概念推广到势为2的非均匀概率空间上;当p∈(0,1)时,证明了全体公式的真度值之集在[0,1]中没有孤立点;利用真度定义公式间的p-相似度和伪距离,进而定义了p-逻辑度量空间,证明了该空间没有孤立点,并在此空间中提出了三种不同类型的近似推理模式。     

基于模糊和证据理论的行为建模

针对人类行为模型系统中存在大量的复杂任务关系和过程,系统输出的概率不确定性等问题,提出了基于Mamdani-Zadeh推理范式和D-S证据理论的建模方法.这种方法把输入空间分割成人们比较容易理解和表达问题的小区域,使用Mamdani-Zadeh和模糊D-S规则控制信度函数的输出值,从而得到了用户预期焦化元的清晰值,实现了模型系统关系、过程简单化;并通过具体例子证实了此方法的精确性和有效性。     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

Verification of evolving software via component substitutability analysis

This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution generates constructive feedback to developers showing how to improve the components. The substitutability approach has been implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark. This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547, and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no. DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.