Problem structuring methods in military command and control

In an authorized military hierarchy organization, the procedure of problem solving must be co-ordinated with the tasks of planning, directing, and controlling. In most combat situations, problem solving knowledge is acquired from an expert (commander) or a single group of experts (staff) in a military organization. Therefore, these multiple actors (commander and the staff), multiple perspectives (multi-expertise and knowledge types), incommensurable and/or conflicting interests (resource allocation and distribution among staff planning), important intangibles (ambiguous quantitative or qualitative apparatus), and key uncertainties (unexpected internal and external situations) are part of unstructured problem. In this article, military strategy and tactics are acquired as case knowledge, rule knowledge, and heuristic knowledge content in terms of representing combat formations and planning mechanism to support problem structuring and the solution of military command and control. By doing so, this article presents a knowledge-based system architecture, including case base, rule base, heuristic base, and learning paradigm, for the military command and control procedure with strategic guidance (commander’s strategy and tactics) and tactical planning (staff plans generation) system functions in terms of implementing problem structuring methods in military command and control.     

大数据技术在协同场景监控中的应用

跨企业信息集成（协同场景监控平台）作为大数据技术在协同场景监控中的创新应用,是企业对内部信息系统协同业务处理的监控中心。该平台整合海南电网公司内部多个信息系统中的海量协同数据,利用大数据技术进行海量数据的采集、存储、处理和展现,实现跨部门、跨业务域的数据资源流动,同时通过大数据分析的结果设计协同监控指标,快速定位各信息系统业务协同中出现的异常情况,辅助公司各级管理人员了解协同问题整体情况,跟进协同问题闭环处理过程,平台结合实际业务需求,设计开发一系列实用功能,利用平台可智能识别并自动推送的相关协同问题和解决方案,利用平台大数据可视化工具可将数据分析结果以丰富的计算机图形直观地进行展示,有效地提高协同问题解决效率,提升企业精益化管理水平。     

基于情景分析的军事物流配送中心选址模型

研究不确定战时情景下的军事物流配送中心选址问题。兼顾军事物流系统性能优化和成本节约建立了两个随机规划模型,模型中引入情景分析,考虑了设施损毁、库容和配送时间约束。讨论了模型求解方法,通过求解模型得到配送中心选址、选型和预置量决策。模型一在情景出现前为每个用户指派了首选与后备供应点,模型二给出了每个情景下的配送方案。仿真算例表明了模型的合理性和解法的有效性,也验证了模型一优于模型二。     

Software systems for situation analysis and decision support on the basis of cognitive maps: Approaches and methods

This paper considers the methods of influence analysis, structure analysis, inverse problem solving and scenario analysis implemented in cognitive map-based modeling software systems. The methods serve for designing control strategies in ill-structured situations.     

Case-based heuristic selection for timetabling problems

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems.     

一种求解Fisher最佳鉴别矢量的新算法及人脸识别

Ｆｉｓｈｅｒ最佳鉴别矢量是高维模式分析中的有效方法,当训练样本数相对于特征空间的维数较小时,就成了小样本问题。为了求解小样本问题,人们提出了一系列方法并取得了的效果。但在类内距离为零的情况下民的方法均得不到最佳解,该文从理论上说明了这一点,并给一种在任何情况下都能得到最佳解的新算法,试验结果这样的推断。     

On analysis of collaborative problem solving: an object-oriented approach

During the last decade an increased interest has been observed on computer-supported collaborative problem solving. This relatively new area of research requires new methodological approaches of interaction and problem solving analysis. Usually analysis of collaborative problem solving situations is done through discourse analysis or interaction analysis, where in the center of attention are the actors involved (students, tutors etc.). An alternative framework, called “Object-oriented Collaboration Analysis Framework (OCAF)” is presented here, according to which the objects of the collaboratively developed solution become the center of attention and are studied as entities that carry their own history. This approach produces a reversed view of the process, according to which the solution is made of structural components that are ‘owned’ by actors who have contributed in various degrees to their development. OCAF provides both qualitative and quantitative measures of collaboration. It is shown that this framework can be applied effectively both in synchronous computer supported collaborative environments of distance groups and in face-to-face collaborative activities.     

Explaining quantitative systems to uninitiated users

The importance of explanation in expert systems has been documented from the early days of their development; there is an equally pressing need for explanation in systems that employ a decision-making process based on quantitative reasoning. This is particularly necessary for users who do not have a sophisticated understanding of the formal apparatus that the system employs to reach its decisions. In order to generate meaningful answers to questions asked by such unsophisticated users, an explanation facility must translate the formal structures of the problem solving system into the concepts with which the user understands the problem domain. Previous work on the explanation of quantitative systems is based on the assumption that the user has at least a basic grasp of the formal approach of the problem solving system. However, in realistic application situations, it is more likely the case that in order for the human user to understand why a mathematically-based advice-giving system makes the suggestions that it does, the problem solving rationale of the system must be explained in the user's own terms, which are typically different from those of the mathematical system. To develop an explanation methodology that is capable of justifying the results of a system based on quantitative reasoning to an uninitiated user, we employ a representation that enables our explanation facility to translate the abstract mathematical relationships that make up a quantitative system into the domain-specific concepts with which a typical user approaches the problem solving task. In our system, the process of generating explanations, therefore, involves translating one set of concepts into another. An added feature of this system is that it is capable of providing explanations from two perspectives: that of the quantitative problem solving system, and that of the human user who is familiar with the domain problem but not with the mathematical approach. We have implemented this approach to explaining quantitative systems by creating an explanation facility for a problem in the manufacturing domain. This facility responds to user queries about a scheduling system that uses a mathematically-based heuristic to choose jobs for an annealing furnace.     

Stability and stabilization of equilibrium positions of nonlinear nonautonomous mechanical systems

Stability analysis methods and stabilizing controls for nonlinear nonautonomous mechanical systems are discussed. Theorems on stability of equilibrium positions in the case of essentially nonlinear positional forces are proved on the basis of decomposition of systems under study. Time estimates for transient processes are found, and the effect of nonstationary perturbations on stability of equilibrium positions is studied. The results obtained are used for solving stabilization problem for mechanical systems with regard to the structure of control forces. Situations where stabilizing controls can be constructed are determined, and the nonlinear forces that play the key role in these situations are identified.     

网络环境下协同虚拟拆卸训练平台

为实现复杂装备拆卸的多人协同工作,规划协同拆卸虚拟训练的工作流程以及网络环境下多人协同拆卸虚拟训练平台的体系框架;采用基于拆卸混合图的协同拆卸串、并行操作的管理方法,并分析协同拆卸过程中的多维拆卸信息,给出各种拆卸信息的协同可视化方法;协同拆卸虚拟训练原型系统的工作过程及其有效性通过示例进行说明并得到验证。     

Redundancy detection in semistructured case bases

With the dramatic proliferation of case-based reasoning systems in commercial applications, many case bases are now becoming legacy systems. They represent a significant portion of an organization's assets, but they are large and difficult to maintain. One of the contributing factors is that these case bases are often large and yet unstructured or semistructured; they are represented in natural language text. Adding to the complexity is the fact that the case bases are often authored and updated by different people from a variety of knowledge sources, making it highly likely for a case base to contain redundant and inconsistent knowledge. We present methods and a system for maintaining large and semistructured case bases. We focus on a difficult problem in case base maintenance: redundancy detection. This problem is particularly pervasive when one deals with a semistructured case base. We discuss an information retrieval-based algorithm and an implemented system for solving this problem. As the ability to contain the knowledge acquisition problem is of paramount importance, our method allows one to express relevant domain expertise for detecting redundancy naturally and effortlessly. Empirical evaluations of the system demonstrate the effectiveness of the methods in several large domains     

An enterprise integration methodology

As more and more organizations pursue the benefits of e-business, they are looking to a process called enterprise integration, or EI, as a key technical enabler in transforming their business processes. A typical form of EI is Webification; in this scenario, a company wants to offer its existing products and services over the Internet, so it builds Web front-end systems and integrates them to its backend legacy systems. (In this article, we use "legacy system" to mean any IT system already in operation.) A more complex El scenario involves enterprise application integration. By this process, the organization links up previously separate and isolated systems to give them greater leverage. For example, an organization might integrate a customer-relationship-management system, a call center system, and legacy customer account systems to give the organization a consolidated view of its customers. An emerging EI scenario is business-to-business (B2B) integration (also called extended enterprise models), which occurs when an organization integrates its own business processes with those of its business partners to improve efficiency within a collaborative value chain.     

Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling

The advent of multiagent systems, a branch of distributed artificial intelligence, introduced a new approach to problem solving through agents interacting in the problem solving process. In this paper, a collaborative framework of a distributed agent-based intelligence system is addressed to control and resolve dynamic scheduling problem of distributed projects for practical purposes. If any delay event occurs, the self-interested activity agent, the major agent for the problem solving of dynamic scheduling in the framework, can automatically cooperate with other agents in real time to solve the problem through a two-stage decision-making process: the fuzzy decision-making process and the compensatory negotiation process. The first stage determines which behavior strategy will be taken by agents while delay event occurs, and prepares to next negotiation process; then the compensatory negotiations among agents are opened related with determination of compensations for respective decisions and strategies, to solve dynamic scheduling problem in the second stage. A prototype system is also developed and simulated with a case to validate the problem solving of distributed dynamic scheduling in the framework.     

Research of new strategies for improving CBR system

Case based reasoning (CBR), as an important AI technology, has gained popularity for its unique means of problem solving, which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. To construct a CBR system, two key issues have to be considered: one is feature selection, through which important features are extracted from the whole experience case and make up a case; the other is case retrieval, through which most appropriate case is retrieved for reuse. In order to further improve the accuracy of CBR system, this paper proposes a new feature selection method called Calculating Differences based on Growing Hierarchical Self Organizing Map clustering (CD-GHSOM) and a new case retrieval method called Growing Hierarchical Self Organizing Map based Case Retrieval (GHSOM-CR). Lots of experiments are implemented to validate the effectiveness of the proposed methods by comparing them with other recent researches.     

Connectionist model for solving static structural problems with fuzzy parameters

In this article, a novel approach for solving static structural problems based on Artificial Neural Network (ANN) has been presented. Various numerical methods are available to solve static problems of structures having crisp parameters by converting the problem to algebraic systems. However, sometimes these structures involve uncertainties. In that case, these problems may not be handled by the usual/existing methods. In the present study, uncertainties are considered as fuzzy. As such, one may get fuzzy linear system of equations. In order to handle these type of fuzzy linear systems, the concept of ANN has been developed. Detail procedure with algorithm has been included for solving the titled problems. Here, various example problems have been examined and results have also been compared with the existing ones in special cases.     

Explanation-based learning:A problem solving perspective

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the problem solving system. 's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. 's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.     

Critic Systems – Towards Human–Computer Collaborative Problem Solving

Human–computer collaboration is extremely necessary for solving ill-structured problems and critic systems can effectively facilitate human–computer collaborative problem solving. This paper conducts a systematic study on critic systems. First, the concepts of critic systems are presented. Then, a literature review is presented on critic systems. Afterwards, a generic architecture is put forward for critic systems, with its important aspects being analyzed. Finally, two case studies are given to illustrate critic systems.     

Two approaches to determining similarity of two digraphs

An approach to solving the problem of determining similarity with application of a maximal common fragment of two graphs is considered. Its two main disadvantages are specified. Two new approaches to solving the problem of determining similarity of digraphs are proposed: a generalized substructural-metric approach and an approach using a stratified system of matrix models of the digraph complexity. New features for investigating similarity of digraphs are formulated. The original problem of calculating similarity of layout of fragments in the digraph is formalized with account of quantitative and qualitative features of fragments of the digraph. A methodology, involving two systems of methods for solving the problem, is developed. The first system of methods takes into account the precise layout of fragments in the digraph, while the second one deals with the approximate layout of fragments. A new class of problems is distinguished, which consists in calculating similarity of digraphs with account of similarity of the layout of fragments of the specified type. An example of solving the problem of finding semantic networks that are most similar to a network-template is presented.