Conceptualizing Structurable Tasks in the Development of Knowledge-Based Systems*

Conventional approaches to knowledge-based system (KBS) development are not appropriate for building KBSs when the application task is structurable (i.e., exhibits a certain degree of ill structure). Building a KBS for structurable tasks requires an understanding of the problem-solving strategies used by an expert to manage the ill structure, while at the same time relying on domain theories to understand the structured parts of the task. This paper presents a methodology for developing a knowledge model for structurable tasks during the conceptualization stage of KBS development. This is equivalent to building a logical model for design during the development of conventional information systems. The methodology relies on prior research on the decomposition and characterization of a task based on its various attributes. The paper also illustrates the use of the methodology in the case of KBS development for financial hedging. The paper concludes with some observations about the potential impact of this methodology on other stages in the KBS development process.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

TWO DESIGN PRINCIPLES FOR KNOWLEDGE-BASED SYSTEMS*

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem-solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role.     

DESIGNING HIGH-CONTACT SERVICE SYSTEMS: APPLICATION TO BRANCHES OF A SAVINGS AND LOAN*

The customer-contact model represents one of the few conceptually based approaches for designing service systems. This paper develops that concept further for high-contact systems, relative to efficiency and effectiveness notions. Four design features (contact technologies, contact training, data bases, and contact roles) are developed and a simple design methodology (built around the four design features) is presented. A demonstration of the design methodology is provided through its applications to branches of a savings and loan. Areas for additional theoretical and practical refinement of the model and the desirability of multidisciplinary research are discussed.     

On current issues in defence systems analysis and combat modelling

Summarizing the discussions of the 1982 symposium sponsored by NATO's Defence Research Group and directed by this author, the paper puts forward some major issues in defence systems analysis and combat modelling related to: (1) the architectural approaches to the design of military campaign simulation models; (2) the verification and validation of models; (3) the employment of combat simulation, field trials and exercises in an exploratory research mode in order to gain insights rather than supporting solutions; (4) the use of interactive combat models to obtain a better understanding of cognitive functions in command and control, especially also with a view to structuring knowledge bases for military expert systems; (5) the type and use of planning models permitting the defence planner to arrive at robust solutions to his problems, not least with regard to possible responses by the potential antagonists.     

基于多种不确定性参数分布的U-GERT网络模型及其应用研究

针对随机网络研究中活动参数的分布是不确定信息的情况,本文提出了不确定信息的GERT网络模型,并利用信号流图原理研究了不确定信息的GERT网络仿真算法;研究了不确定变量GERT网络的矩母函数的构造,并对其重要的性质做了详细的探讨;最后通过算例仿真,验证本文提出的不确定信息的GERT网络模型及其仿真算法能够有效的解决现实生活中不确定信息对GERT网络参数的影响,说明模型与算法正确可行。     

Characterizing performance in socio-technical systems: A modeling framework in the domain of nuclear power

In modeling complex socio-technical systems, the critical nature of many interactions between human and automation are often unrecognized until the system experiences catastrophic consequences. One reason for this is that the systems are generally very robust and serious problems are rare. Another reason is the difficulty in modeling the variety of interactions in a manner compatible with analytical and/or computational models of the technological components of the system. Organizational issues and extraneous influences add to these modeling difficulties. We have developed a modeling framework that integrates certain ideas from social networks, graph theory, and simulation, all tied together using heuristics that reflect deep domain knowledge. This framework forms the basis for a computational implementation, portions of which have been implemented. Details of the model and the rationale for the model structure are described in this paper.     

Local and Global Knowledge Complementarity: R&D Collaborations and Innovation of Foreign and Domestic Firms

We analyze how research and development (R&D) collaborations affect product innovation for subsidiaries of foreign multinational firms and domestic firms. We build on the knowledge-based view to propose that subsidiaries and domestic firms differ in their ability to benefit from alternative R&D partners as a result of the variation in their knowledge complementarities. Specifically, we propose that subsidiaries may benefit more from undertaking R&D collaborations with customers and competitors, whose deeper knowledge of local conditions better complements the more global knowledge base of subsidiaries. In contrast, we argue that domestic firms may benefit more from engaging in R&D collaborations with suppliers and universities, whose more global nature of knowledge better complement the deeper local knowledge base of domestic firms.     

Expert knowledge,expert systems and commercial interests

Experience to date in building expert systems has brought a general realization of the complexity of the effort required for producing systems capable of operating on ‘real-world’ problems. The most successful knowledge-based/expert systems built to date, e.g. DENDRAL, MYCIN, PROSPECTOR and R1 have demonstrated that a considerable investment in time and dedication on the part of systems designers and experts is required to create a fully operational system. The great majority of development projects attempting to harness this new programming technology do not extend beyond creation of a prototype system largely due to lack of necessary resources, i.e. time, money and know-how. The purpose of this paper is to discuss some of the more important issues associated with development of expert systems and to provide an overview of the commercial and industrial efforts of putting expert systems to work. This paper should be of interest to those who have gained their knowledge of expert systems from the reading of non-specialist publications and thus may have been exposed to somewhat over enthusiastic accounts of the subject.     

Choosing the Right Expert System Building Approach

The analytical hierarchy process (AHP) is used to systematically examine the relative appropriateness of contending expert system building approaches—algorithmic programming languages, symbolic programming languages, expert system shells, and knowledge engineering environments based upon characteristics of application domains and development environments. Two expert system projects, Back Expert, and R&D Expert, illustrate the value of this methodology. Domain and development environment characteristics identified in the AHP approach are then used to construct rule-based and Lotus 1-2-3 expert systems for selecting the right expert system building approach.     

System of Systems Engineering and Risk Management of Extreme Events: Concepts and Case Study

The domain of risk analysis is expanded to consider strategic interactions among multiple participants in the management of extreme risk in a system of systems. These risks are fraught with complexity, ambiguity, and uncertainty, which pose challenges in how participants perceive, understand, and manage risk of extreme events. In the case of extreme events affecting a system of systems, cause‐and‐effect relationships among initiating events and losses may be difficult to ascertain due to interactions of multiple systems and participants (complexity). Moreover, selection of threats, hazards, and consequences on which to focus may be unclear or contentious to participants within multiple interacting systems (ambiguity). Finally, all types of risk, by definition, involve potential losses due to uncertain events (uncertainty). Therefore, risk analysis of extreme events affecting a system of systems should address complex, ambiguous, and uncertain aspects of extreme risk. To accomplish this, a system of systems engineering methodology for risk analysis is proposed as a general approach to address extreme risk in a system of systems. Our contribution is an integrative and adaptive systems methodology to analyze risk such that strategic interactions among multiple participants are considered. A practical application of the system of systems engineering methodology is demonstrated in part by a case study of a maritime infrastructure system of systems interface, namely, the Straits of Malacca and Singapore.     

Managing the Risk of Uncertain Threshold Responses: Comparison of Robust, Optimum, and Precautionary Approaches

Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for decision making under uncertainty -- optimal expected utility, the precautionary principle, and three different approaches to robust decision making -- for addressing the challenge of adding pollution to a lake without triggering unwanted and potentially irreversible eutrophication. The three robust decision approaches -- trading some optimal performance for less sensitivity to assumptions, satisficing over a wide range of futures, and keeping options open -- are found to identify similar strategies as the most robust choice. This study also suggests that these robust decision approaches offer a quantitative, decision analytic framework that captures the spirit of the precautionary principle while addressing some of its shortcomings. Finally, this study finds that robust strategies may be preferable to optimum strategies when the uncertainty is sufficiently deep and the set of alternative policy options is sufficiently rich.     

A Knowledge Based Decision Support System for Setup Reduction

The issue of setup reduction is important for firms seeking to incorporate advanced procedures and concepts such as flexible manufacturing systems (FMS) and just-in-time (JIT) manufacturing to improve manufacturing productivity. Investing adequate amounts in setup reduction is a complex decision affected by many factors including the existing level of automation, worker characteristics, product features, and the manufacturing environment. The consequences of improper investment (i.e., over- and underinvestment) includes possible disruption of manufacturing operations as well as wasted resources. Much of the knowledge involved in the investment decision is heuristic and experience-based, and takes on greater significance when the form of the setup reduction function is not known a priori. This paper describes the development of a knowledge-based decision support system (KBDSS) that combines both heuristic and procedural knowledge to provide support for the setup reduction investment decision. The system uses codified manufacturing expertise to improve both the accuracy and effectiveness of setup-reduction decision making. Use of the KBDSS is illustrated by two examples based on disparate manufacturing contexts.     

The knowledge-based economy: intellectual origins and new economic perspectives

This paper discusses the intellectual origins of the knowledge-based economy or KBE, and recent economic theories used to provide an intellectual foundation for the KBE. The KBE is the dominant post-industrial economic development paradigm that emerged in the 1980s, with an emphasis on the role of knowledge creation and distribution as the primary driver in the process of economic growth, the distribution of income, the growing importance of knowledge-based networks among firms, and the interface between government business and citizens in the advanced economies. Recent empirical evidence on rates of return, geographic spillovers, and the internationalization of knowledge flows are discussed in relation to the theoretical foundations of the KBE.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.     

基于知识图谱与扎根分析的中国管理理论构建范式研究述评

基于知识图谱理论和扎根理论的方法指导,以中文社会科学引文检索数据库收录的1998~2019年期间,与中国管理研究以及理论构建相关的561篇施引文献和7108篇被引文献作为研究对象。通过运用Citespace V可视化软件,进行中国管理研究与理论构建的知识基础、研究热点和主题演进脉络等的可视化分析与研究;并进一步精选了116篇科技文献为样本数据进行三级扎根编码,构建中国管理理论的研究框架模型。研究表明:中国管理研究目前聚焦于理论构建范式的研究主题,构建中国管理理论要直面实践导向,扎根传统文化,实现实践与文化的融合,要采用多元科学方法论与方法,辩证地使用实证研究方法。     

Opportunities for research about managing the knowledge-based enterprise

Potentially valuable directions for new research into the management of knowledge-based enterprises are identified in this paper. This was done by reviewing relevant literature to develop research questions, using a model of knowledge-based capabilities to focus the review. The model highlights six knowledge capabilities: acquisition, creation, capture, storage, diffusion and transfer. A knowledge-based enterprise would have to engage in (if not excel at) these activities simply to manage its key resource – knowledge. Forty-two research questions were proposed based on the review. The focus of the research questions varies widely, representing potential opportunities for researchers from many different areas to further our understanding of managing knowledge-based enterprises.     

Addressing Climate Change as an Emerging Risk to Infrastructure Systems

The consequences that climate change could have on infrastructure systems are potentially severe but highly uncertain. This should make risk analysis a natural framework for climate adaptation in infrastructure systems. However, many aspects of climate change, such as weak background knowledge and societal controversy, make it an emerging risk where traditional approaches for risk assessment and management cannot be confidently employed. A number of research developments aimed at addressing these issues have emerged in recent years, such as the development of probabilistic climate projections, climate services, and robust decision frameworks. However, additional research is needed to improve the suitability of these methods for infrastructure planning. In this perspective, we outline some of the challenges in addressing climate change risks to infrastructure and summarize new developments aimed at meeting these challenges. We end by highlighting needs for future research, many of which could be well‐served by expertise within the risk analysis community.     

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Cluster‐based segmentation usually involves two sets of variables: (i) the needs‐based variables (referred to as the bases variables), which are used in developing the original segments to identify the value, and (ii) the classification or background variables, which are used to profile or target the customers. The managers’ goal is to utilize these two sets of variables in the most efficient manner. Pragmatic managerial interests recognize the underlying need to start shifting from methodologies that obtain highly precise value‐based segments but may be of limited practical use as they provide less targetable segments. Consequently, the imperative is to shift toward newer segmentation approaches that provide greater focus on targetable segments while maintaining homogeneity. This requires dual objective segmentation, which is a combinatorially difficult problem. Hence, we propose and examine a new evolutionary methodology based on genetic algorithms to address this problem. We show, based on a large‐scale Monte Carlo simulation and a case study, that the proposed approach consistently outperforms the existing methods for a wide variety of problem instances. We are able to obtain statistically significant and managerially important improvements in targetability with little diminution in the identifiability of value‐based segments. Moreover, the proposed methodology provides a set of good solutions, unlike existing methodologies that provide a single solution. We also show how these good solutions can be used to plot an efficient Pareto frontier. Finally, we present useful insights that would help managers in implementing the proposed solution approach effectively.