A case-based reasoning approach to cognitive map-driven tacitknowledge management

Knowledge is at the heart of knowledge management. In literature, a lot of studies have been suggested covering the role of knowledge in improving the performance of management. However, there are few studies about investigating knowledge itself in the arena of knowledge management. Knowledge circulating in an organization may be explicit or tacit. Until now, literature in knowledge management shows that it has mainly focused on explicit knowledge. On the other hand, tacit knowledge plays an important role in the success of knowledge management. It is relatively hard to formalize and reuse tacit knowledge. Therefore, research proposing the explication and reuse of tacit knowledge would contribute significantly to knowledge management research. In this sense, we propose using cognitive map (CM) as a main vehicle of formalizing tacit knowledge, and case-based reasoning as a tool for storing CM-driven tacit knowledge in the form of frame-typed cases, and retrieving appropriate tacit knowledge from the case base according to a new problem. Our proposed methodology was applied to a credit analysis problem in which decision-makers need tacit knowledge to assess whether a firm under consideration is healthy or not. Experiment results showed that our methodology for tacit knowledge management can provide decision makers with robust knowledge-based support.     

Assessing the cognitive consequences of the object-oriented approach: A survey of empirical research on object-oriented design by individuals and teams

This paper presents a state-of-the-art review of empirical research on object-oriented (OO) design. Many claims about the cognitive benefits of the OO paradigm have been made by its advocates. These claims concern the ease of designing and reusing software at the individual level as well as the benefits of this paradigm at the team level. Since these claims are cognitive in nature, it seems important to assess them empirically. After a brief presentation of the main concepts of the OO paradigm, the claims about the superiority of OO design are outlined.The core of this paper consists of a review of empirical studies of OO design (OOD). We first discuss results concerning OOD by individuals. On the basis of empirical work, we (1) analyse the design activity of novice OO designers, (2) compare OOD with procedural design and (3) discuss a typology of problems relevant for the OO approach. Then we assess the claims about naturalness and ease of OOD. The next part discusses results on OO software reuse. On the basis of empirical work, we (1) compare reuse in the OO versus the procedural paradigm, (2) discuss the potential for OO software reuse and (3) analyse reuse activity in the OO paradigm. Then we assess claims on reusability. The final part reviews empirical work on OOD by teams. We present results on communication, coordination, knowledge dissemination and interactions with clients. Then we assess claims about OOD at the software design team level.In a general conclusion, we discuss the limitations of these studies and give some directions for future research.     

Deriving engineering software from requirements

With the advent of intelligent computer aided design systems, companies such as Boeing are embarking on an era in which core competitive engineering knowledge and design rationale is being encoded in software systems. The promise of this technology is that this knowledge can be leveraged across many different designs, product families, and even different uses (e.g., generative process planning for manufacturing). The current state of the practice attempts to achieve this goal through the reuse of software components. A fundamental problem with this approach to knowledge sharing and reuse is that what we are trying to reuse is software—the end artifact in a long and complicated process that goes from requirement specifications, through a process of design, to implementations. Knowledge sharing and reuse can not easily and uniformly occur at the software level. So what can be done as an alternative? This paper describes a theory, methodology, language, and tool for the semi-automatic development and maintenance of engineering software from requirement specifications. In essence, this paradigm for software development and maintenance is one that explicitly captures requirement specifications, designs, implementations, and the refinement processes that lead from requirements all the way down to software. By recording this entire refinement history, we stand a better chance of leveraging knowledge for different uses.     

一个基于实例推理的软件过程知识复用方法

过程资产库的建立和基于过程资产的项目过程定义是一个组织的软件能力成熟度达到已定义级的关键标志,传统的过程资产库中存储的过程往往是通过文字描述和使用者判断其适用性,用于项目过程的建立。本文提出了一种基于实例推理(CBR)的过程知识复用方法,通过过程特征的刻画和实例相似度计算,实现过程实例的提取和复用。实践证明,该方法有助于提高过程知识复用的准确度和自动化程度,为软件过程改进中的过程建立提供有效手段。     

Reuse-Conducive Development Environments

Despite its well-recognized benefits, software reuse has not met its expected success due to technical, cognitive, and social difficulties. We have systematically analyzed the reuse problem (especially the cognitive and social difficulties faced by software developers who reuse) from a multidimensional perspective, drawing on our long-term research on information retrieval, human-computer interaction, and knowledge-based systems. Based on this analysis, we propose the concept of reuse-conducive development environments, which encourage and enable software developers to reuse through the smooth integration of reuse repository systems and development environments. We have designed, implemented, and evaluated CodeBroker—a reuse-conducive development environment—that autonomously locates and delivers task-relevant and personalized components into the current software development environment. Empirical evaluations of CodeBroker have shown that the system is effective in promoting reuse by enabling software developers to reuse components unknown to them, reducing the difficulties in locating components, and augmenting the programming capability of software developers.     

Build-level components

Reuse between software systems is often not optimal. An important reason is that while at the functional level well-known modularization principles are applied for structuring functionality in modules, this is not the case at the build level for structuring files in directories. This leads to a situation where files are entangled in directory hierarchies and build processes, making it hard to extract functionality and to make functionality suitable for reuse. Consequently, software may not come available for reuse at all, or only in rather large chunks of functionality, which may lead to extra software dependencies. In this paper, we propose to improve this situation by applying component-based software engineering (CBSE) principles to the build level. We discuss how existing software systems break CBSE principles, we introduce the notion of build-level components, and we define rules for developing such components. To make our techniques feasible, we define a reengineering process for semiautomatically transforming existing software systems into build-level components. Our techniques are demonstrated in two case studies where we decouple the source tree of Graphviz into 46 build-level components and analyze the source tree of Mozilla.     

A cognitive science perspective on knowledge acquisition

Looking back over 25 yrs of R&D in knowledge acquisition – particularly mediated by workshops and a journal founded by Brian Gaines – it is remarkable how topics and perspectives have changed. We started from the assumption that the knowledge acquisition bottleneck was due to the problems of experts expressing their expert knowledge. However, explicitly available knowledge, as, e.g. in handbooks, appeared often sufficient to construct an adequate ‘artificial problem solver’. Expert systems became knowledge systems. The bottleneck was rather experienced in modeling the domain knowledge and the reasoning control – the problem solving method (PSM) – for automated execution. By developing libraries of reusable PSMs, or implementing shells with built-in PSMs, this bottleneck could be eased. Specifications of domain knowledge lead to the development of ontologies: another source for reuse. Despite the fact that this brought sufficient technology for a mature engineering methodology, little use is made of it in current practice, and developing ‘intelligent’ software is not much different today from what it was at the beginning of the 1980s. A more impressive heritage of knowledge acquisition R&D is the introduction of technology for building ontologies. These found their way via knowledge management to the architecture of the Semantic Web. In research apparent solutions also bring new problems. Two of these problems are suggested by empirical cognitive science research. The first one is that knowledge as represented in currently available (top-level) ontologies are too simple, because high level concepts may come in design ‘patterns’: a view that has recently also been taken up in the knowledge acquisition community. The second problem is the fact that despite much empirical research in cognitive psychology, we have insufficient insight in how we acquire new conceptualisations from text. This is a serious bottleneck for an early dream in knowledge acquisition: automated knowledge acquisition.     

A Case-Addition Policy for Case-Base Maintenance

A major problem in many practical applications of case-based reasoning (CBR) and knowledge reuse is how to keep the case bases concise and complete. To solve this problem requires repeated maintenance operations to be applied to case bases. Different maintenance policies may result in case bases with very different quality. In this article, we present a case-addition maintenance policy that is guaranteed to return a concise case base with good coverage quality. We demonstrate that the coverage of the case base computed by the case-addition algorithm is no worse than the optimal case-base coverage by a fixed lower bound. We also show that the algorithm implementing the case-addition policy is efficient. Our result also highlights benefit reduction as a key factor in influencing the convergence of case-base coverage when cases are added to a case base. Through our theoretical analysis, we analytically derive the well known coverage convergence curves commonly displayed in CBR experiments and show that benefit reduction can be used as a predictor for convergence speed.     

Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.     

基于本体的案例推理模型研究*

提出了基于本体的案例检索及相似性评估方法和基于本体的案例适配模型,使得CBR(case-based reasoning)系统的开发可在语义层次上进行相似性评估和案例适配,这样得到的结果更能反映用户的真实需求;并且CBR所需要的领域知识可从本体中获取,大大降低了传统CBR系统中知识获取的瓶颈。最后在此基础上,提出了基于本体的CBR系统模型框架,从软件复用的角度提高了CBR系统的开发效率。     

Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.     

Principal–agent problem: a cognitive map approach

As software agents become more and more intelligent, it becomes more and more difficult for human principals to understand and control them. This is a well-known principal–agent problem. There is, thus, need for a tool that can bridge the gap between human principals and software agents. In this paper, we discuss a new approach based on cognitive map to understand and control the knowledge of software agents. We propose a hierarchical framework to construct cognitive map from the rule base of software agents with the help of some guidelines. We applied the cognitive map approach to the famous Pursuit problem and demonstrated how learning can takes place with the help of cognitive maps. In this study, we found that cognitive map could be a promising tool for understanding and controlling intelligent software agents.     

Competence Models and the Maintenance Problem

Case-based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems.     

A Data Mining Approach for Detecting Higher-Level Clones in Software

Code clones are similar program structures recurring in variant forms in software system(s). Several techniques have been proposed to detect similar code fragments in software, so-called simple clones. Identification and subsequent unification of simple clones is beneficial in software maintenance. Even further gains can be obtained by elevating the level of code clone analysis. We observed that recurring patterns of simple clones often indicate the presence of interesting higher-level similarities that we call structural clones. Structural clones show a bigger picture of similarity situation than simple clones alone. Being logical groups of simple clones, structural clones alleviate the problem of huge number of clones typically reported by simple clone detection tools, a problem that is often dealt with postdetection visualization techniques. Detection of structural clones can help in understanding the design of the system for better maintenance and in reengineering for reuse, among other uses. In this paper, we propose a technique to detect some useful types of structural clones. The novelty of our approach includes the formulation of the structural clone concept and the application of data mining techniques to detect these higher-level similarities. We describe a tool called Clone Miner that implements our proposed technique. We assess the usefulness and scalability of the proposed techniques via several case studies. We discuss various usage scenarios to demonstrate in what ways the knowledge of structural clones adds value to the analysis based on simple clones alone.     

大规模定制环境下的决策案例库系统

针对大规模定制决策的特点,为实现决策案例的重用,提出了一种面向大规模定制决策问题的案例库系统,设计了基于框架结构的案例知识表示模型,介绍了基于模糊加权的案例相似度计算和匹配算法,该算法解决了大规模定制决策问题的结构化表达、检索匹配和重用问题,开发了案例库的原型系统以支持案例管理、推理和基于案例的规则发现。     

The IKBALS project: Multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers.Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process.Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions.The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.     

软件工程知识的显著复用影响因素识别方法

软件工程领域的知识复用,有助于改进软件过程整体实施的质量。选用高适用度的软件工程知识,能够为软件项目开发构造坚实的实施基础。情形是软件工程知识得以共享和复用的重要基础。然而,一方面,当前软件工程知识复用涉及众多实际情形因素;另一方面软件工程知识已经呈现出丰富、多样化的事态。选用高适用的软件工程知识的一个复杂性问题是如何确定影响其复用效果的显著情形要素。为缓解此问题,研究了一种基于统计学的软件工程知识的显著复用影响因素识别方法,用以识别软件工程知识适用性的关键情形因素。首先归纳了相关的数学概念,提出了复用影响显著性系数判定的两种指标;然后提出了利用统计数据度量两种指标的度量计算方法以及在此基础上求解显著情形要素权重的计算方法;最后将这种方法应用在原型法知识复用影响因素的识别问题上,应用结果表明这种方法具有较好的可操作性及实用性。     

Software maintenance seen as a knowledge management issue

Creating and maintaining software systems is a knowledge intensive task. One needs to have a good understanding of the application domain, the problem to solve and all its requirements, the software process used, technical details of the programming language(s), the system’s architecture and how the different parts fit together, how the system interacts with its environment, etc. All this knowledge is difficult and costly to gather. It is also difficult to store and usually lives only in the mind of the software engineers who worked on a particular project.If this is a problem for development of new software, it is even more for maintenance, when one must rediscover lost information of an abstract nature from legacy source code among a swarm of unrelated details.In this paper, we submit that this lack of knowledge is one of the prominent problems in software maintenance. To try to solve this problem, we adapted a knowledge extraction technique to the knowledge needs specific to software maintenance. We explain how we explicit the knowledge discovered on a legacy software during maintenance so that it may be recorded for future use. Some applications on industry maintenance projects are reported.