A hybrid knowledge-based decision support system for enterprise mergers and acquisitions

This paper presents a hybrid knowledge-based decision support system for enterprise mergers and acquisitions. The HKDSS system provides not only practical merger and acquisition procedures, related regulations, and legal problems but also feasible solutions or actions through the performance of rule-based reasoning. The hybrid system integrates a database, case base, rule base and model base to create a tool that managers can use to deal with decision-making problems via the Internet. Rules in the rule base are explained here in detail to illustrate the process of reasoning, and four practical cases in the case base also used to show how a case can be dealt with as a new case arrives. In order to evaluate a business's value, a discounted free cash flow model and economic profit model in the model base have been developed. Finally, a scenario analysis is employed to show how the user can deal with an uncertain growth rate and present value.     

一个基于知识库及其自动推理机制的ICAI(化学)系统

设计并实现了一种新型的ＩＣＡＩ系统。该系统以自动推理技术为基础,利用化学知识库,不仅实现了ＩＣＡＩ的知识集散功能,还成功地实现了知识揭示功能,不同于常垢ＩＣＡＩ的单纯检索－播放作用,该系统还具有化学用语层次上的人－机交互、基于自动推理的化学式与中文名称的双向翻译、化学问题求解等多多种智能功能。该系统还具有开放性,可以让用户在使用中调整系统的性能、补充存储的知识信息。该文比较了新型系统与流行的常规系     

Hybrid job offer recommender system in a social network

Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.     

Mammographic case base applied for supporting image diagnosis of breast lesion

Mammography is an important screening tool for early detection of breast cancer. However, radiologists usually experience difficulties in image interpretation of grey zones. A computer system providing similar cases with known diagnostic results for decision support would be useful. Applying case-based reasoning (CBR) to a mammographic case base, constructed from prior cases with known diagnostic results, offers a solution to this problem. Serving as an inference tool, the CBR can retrieve similar cases to help radiologists interpret a new mammographic case. To evaluate the usability of this system, 34 licensed radiologists were invited as experts to assess the system. The results indicate that CBR applied to the mammographic case base is valuable for decision support in mammographic image interpretation.     

A Case Based System for Oil and Gas Well Design with Risk Assessment

A case base system for a complex problem like oil field design needs to be richer than the usual case based reasoning system. Genesis, the system described in this paper contains large heterogeneous cases with metalevel knowledge. A multi-level indexing scheme with both preallocated and dynamically computed indexing capability has been implemented. A user interface allows dynamic creation of similarity measures based on modelling of the users intentions. Both user aiding and problem solution facilities are supported, a novel feature is that risk estimates are also provided. Performance testing indicates that the case base produces on average, better predictions for new well developments than company experts. Early versions of the system have been deployed into oil companies in 6 countries around the world and research is continuing on refining the system in response to industry feedback.     

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Evaluation and selection of the software packages is complicated and time consuming decision making process. Selection of inappropriate software package can turn out to be costly and adversely affects business processes and functioning of the organization. In this paper we describe (i) generic methodology for software selection, (ii) software evaluation criteria, and (iii) hybrid knowledge based system (HKBS) approach to assist decision makers in evaluation and selection of the software packages. The proposed HKBS approach employs an integrated rule based and case based reasoning techniques. Rule based reasoning is used to capture user needs of the software package and formulate a problem case. Case based reasoning is used to retrieve and compare candidate software packages with the user needs of the package. This paper also evaluates and compares HKBS approach with the widely used existing software evaluation techniques such as analytic hierarchy process (AHP) and weighted scoring method (WSM).     

铁路超限超重货物装载加固可拓实例推理方法

铁路超限超重货物具有长大、笨重和价值昂贵等特征,装载加固影响因素众多且无法完全量化表达,超限超重货物装载加固决策问题是一个半结构化问题,设计装载加固可拓实例推理技术对提升铁路超限超重货物安全装载水平和运输质量尤为重要。结合铁路超限超重货物特征及其装载加固决策要素,采用可拓基元与实例推理技术,构造超限超重货物装载加固实例推理基础数据与推理规则模块的可拓基元模型,分析装载加固可拓实例属性取值特征,给出局部与全局相似度计算公式,设计超限超重货物装载加固可拓实例推理算法,确定待解实例的解。实例运用表明所给出的可拓实例推理方法可制定出合理安全的装载加固方案,切实有效解决铁路超限超重货物装载加固决策问题。     

The inseparability problem in interactive case-based reasoning

In applications of interactive case-based reasoning (CBR) such as help-desk support and recommender systems, a problem that often affects retrieval performance is the inability to distinguish between cases that have different solutions. For example, it is not unusual in recommender systems for two distinct products or services to have the same values for all attributes in the case library. While it is unlikely that both solutions are equally suited to the user's requirements, the system cannot help the user to choose between them. This problem, which we refer to as inseparability, can also arise as a result of incomplete data in the target problem presented for solution by a CBR system. We present an in-depth analysis of the inseparability problem, its relationship to the problem of incomplete data, and its impact on retrieval performance.     

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.     

A framework for building knowledge-bases under uncertainty

Managing uncertainty during the knowledge engineering process from elicitation to validation and verification requires a flexible, intuitive, and semantically sound knowledge representation. This is especially important since this process is typically highly interactive with the human user to add, update, and maintain knowledge. In this paper, we present a model of knowledge representation called Bayesian Knowledge-Bases (BKBs). It unifies a ‘if-then’ style rules with probability theory. We also consider the computational efficiency of reasoning over BKBs. We can show that through careful construction of the knowledge-base, reasoning is computationally tractable and can in fact be polynomial-time. BKBs are currently fielded in the PESKI intelligent system development environment.     

交互式群范例学习问题的求解及应用

针对传统基于范例推理方法存在的缺陷以及实际决策问题的需求,提出了一种交互式基于群范例学习的问题求解技术,给出了这一技术的问题求解过程、求解算法,针对算法中涉及到的范例检索方法,给出了一种基于2级索引的范例检索算法。弥补了传统CBR方法的缺陷,满足了解决实际决策问题的需要,展望了它的应用。     

Pattern-Based Interactive Diagnosis of Multiple Disorders: The MEDAS System

A knowledge-based interactive sequential diagnostic system is introduced which provides for diagnosis of multiple disorders in several body systems. The knowledge base consists of disorder patterns in a hierarchical structure that constitute the background medical information required for diagnosis in the domain under consideration (emergency and critical care medicine, in our case). Utilizing this knowledge base, the diagnostic process is driven by a multimembership classification algorithm for diagnostic assessment as well as for information acquisition [1]. A key characteristic of the system is congenial man-machine interface which comes to expression in, for instance, the flexibility it offers to the user in controlling its operation. At any stage of the diagnostic process the user may decide on an operation strategy that varies from full user control, through mixed initiative to full system control. Likewise, the system is capable of explaining to the user the reasoning process for its decisions. The model is independent of the knowledge base, thereby permitting continuous update of the knowledge base, as well as expansions to include disorders from other disciplines. The information structure lends itself to compact storage and provides for efflcient computation. Presently, the system contains 53 high-level disorders which are diagnosed by means of 587 medical findings.     

A probabilistic reasoning model: Formulation and control strategy

It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.     

Mining competent case bases for case-based reasoning

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets.     

RespiDiag: A Case-Based Reasoning System for the Diagnosis of Chronic Obstructive Pulmonary Disease

In this paper a decision support system for the diagnosis of a very serious respiratory disease caused by tobacco named the chronic obstructive pulmonary disease is presented. The system is based on case-based reasoning principles and gathers the experience of experts of the pneumology department of Dorban Hospital (Annaba, Algeria). A critical issue about the case base is that some values of the features are missing in most cases. Five approaches for managing this problem of missing data are proposed. Three of them allow evaluating the similarity despite the missing information. The two other approaches fill the voids with plausible values using a statistical method and the principles of case-based reasoning itself.     

A utility range-based interactive group support system for multiattribute decision making

This research suggests an interactive procedure for solving a multiattribute group decision problem using a range-typed utility information, and develops an interactive group support system (RINGS) to implement some capabilities of the procedure. It is often difficult for group members to articulate their preferences with cardinal values, instead they prefer to give incomplete information only. Utility ranges are obtained by solving linear programming problems with incompletely specified information. RINGS finds out conflicting opinions among group members, compares each member's preferences with the others’, and suggests a direction for consensus seeking. The procedure of RINGS utilizes range-typed visual information, which helps the group reach a consensus. RINGS has several facilities like graphic user interface, model base, and database, so the total time to reach a group consensus is reduced. The evaluation of information systems is illustrated as a demonstration of some capabilities of RINGS.Scope and purposeThis paper deals with the multiple attribute decision-making problem using a separable linear programming technique when group decision makers give only their incomplete information about the attribute weight and alternative value. The utility range for individual decision maker is calculated from each decision maker's incomplete information, and the utility range for group decision makers is obtained from those of individual decision makers. An interactive procedure for establishing a group's pairwise dominance relations with a group's utility range is described, and an interactive group support system (RINGS) is developed to implement the procedure. A case study about the evaluation of information systems is illustrated as a demonstration of some capabilities of RINGS.     

Visual reasoning instructional software system

Visual reasoning is an essential skill for an engineer to possess, particularly as computer-aided design tools become more prevalent. In this paper, we describe an innovative interactive multimedia application that provides a student with the tools to build a strong foundation in visual reasoning. The system, called the Visual Reasoning Tutor, exploits the missing view problem as a mechanism to develop the visual reasoning abilities of students. The Visual Reasoning Tutor provides a student with interactive, graphical operations to construct 3-D geometric objects, varying levels of intelligent critiques throughout the solution process, and a graphical user interface which supports multimedia capabilities aimed to enhance the learning process. This paper presents an overview of the system components, a complete sample exercise, and testing results of several prototypes that have been used in the classroom.