基于CBR与RBR的设备采购决策支持系统

该文通过分析制造行业新产品试制部门的设备采购过程,说明采购决策支持系统引入的必要性,并基于CBR(Case-BasedReasoning)案例推理和RBR(Rule-BasedReasoning)规则推理,构造了混合框架的推理系统及相应的案例表示结构,解决了设备采购等复杂决策领域中决策支持系统冗余推理的问题。最后将该混合推理框架及案例表示结构应用于某大型制造企业试制部门的决策支持系统中,取得了较好的效果。     

融合案例推理与规则推理的设备采购决策支持系统

对制造行业新产品试制部门的设备采购过程进行了分析，指出其对于整个企业制造过程的重要性，说明采购决策支持系统的引入的必要性，并将基于案例推理与基于规则推理相结合，构造了混合框架的推理系统及相应的案例表示结构，解决了设备采购等复杂决策领域中决策支持系统冗余推理的问题。最后将该混合推理框架及案例表示结构应用于某大型跨国制造企业试制部门的决策支持系统中，取得了较好的效果。     

基于案例库的应急决策支持系统研究

文结合CBR和决策支持系统,构建了基于案例库的应急决策支持系统的体系结构,并对基于案例推理的一些关键技术做了初步的探讨。     

基于CBR的通信对抗指挥IDSS设计

研究了基于案例推理的通信对抗指挥智能决策支持系统开发技术。在分析通信对抗指挥决策流程的基础上,基于案例推理的设计思想,提出了一个通信对抗指挥智能决策支持系统的技术框架。该框架划分为应用层、处理层、管理层、信息层和通信层,同时讨论了各层的功能及详细内容。该系统克服了传统的基于规则推理系统的军事知识难于获取、表示和推理的脆弱性等缺陷,能有效地处理通信对抗指挥中面临的半结构化或非结构化问题,提高了制定通信对抗方案的时效性和科学性。     

基于CBR应急保障物流体智能决策支持系统研究

在对应急决策和应急保障物流体分析的基础上,应用基于案例推理技术和智能决策方法构建了应急保障物流体智能决策支持系统,讨论了系统的工作原理及体系结构,重点分析了系统的案例推理机制和关键技术,从而为应急状态下的物流保障决策和原形系统的开发提供了理论支撑。     

案例决策技术及案例决策支持系统研究综述

案例是人类直觉、逻辑和创造性三种思维的综合表现形式,案例推理是人脑类比学习的模仿者,在知识难以获取的应用领域取得了丰硕的成果.从认知学角度探讨了案例推理的逻辑合理性,研究了案例智能决策技术;针对决策支持系统研究与开发中出现的系统柔性差等核心问题,提出了以案例推理为基础的案例智能决策支持系统,该系统可以很好地实现决策支持.对此进行了深入研究,最后探讨了该系统的研究与发展的方向.     

湖北省突发事件预警应急智能决策支持系统

介绍了突发事件预警应急智能决策支持系统研究的必要性,阐述了湖北省突发事件预警应急智能决策支持系统的网络结构、系统的基本功能和系统的结构设计思想。重点讨论了该系统的关键技术——主推理机的设计。为解决案例推理和规则推理方面的不足,提出了一种新的FCR算法,它将案例推理、规则推理和模糊推理有机结合起来,并用于实际的智能决策支持系统。     

船舶避碰智能决策支持系统研究

建立了一个基于RBR(Case-Based Reasoning,基于案例推理)和CBR(Rule-Based Reasoning,基于规则推理)结合的船舶避碰决策支持模型,将这个模型引入到船舶避碰智能决策支持系统IDSSVCA(Intelligent Decision Support System for Vessel...     

基于CBR的交通事故处理智能决策支持系统

依据智能化、集成化、协调化和网络化的设计准则,给出了基于CBR的交通事故处理智能决策支持系统的总体框架。并对该系统中的关键技术问题,基于案例推理的综合推理模型、典型案例库系统、人机智能系统分别进行了讨论。     

基于案例和描述逻辑的海上援救规划方法

实现知识共享和案例检索是开发基于案例的智能决策支持系统面临的首要问题。针对海上援救指挥决策,采用Web本体语言OWL表示危机态势信息和海上援救领域知识,提出一种基于案例推理和描述逻辑推理的援救规划业务流程。以援救搁浅触礁舰船为例,对海损状况及其援救方法等信息进行了描述和推理。实验结果表明,根据具有语义的领域知识使用描述逻辑推理机对危机态势信息进行分类,能事先限定所要搜索的案例库和选择合理的援救方法,并提高案例检索效率和援救规划的实用性。     

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.     

Interactive Case-Based Planning for Forest Fire Management

This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.     

A knowledge-based supplier intelligence retrieval system for outsource manufacturing

Knowledge management is to promote business success through a formal, structured initiative to improve the use of knowledge in an organization, in which an effective organizational memory information system plays an increasingly important role. Unlike the past, the performance of an enterprise now depends much on the performance and relationship of its customer–suppliers in the value chain. Good customer–supplier relationships are important for an organization to respond to dynamic and unpredictable changes. This paper describes a knowledge-based supplier selection and evaluation system, which is a case-based reasoning decision support system for outsourcing operations at Honeywell Consumer Products (Hong Kong) Limited in China. As a result, collaborative suppliers are identified quickly during the new product development process. By using the system, the cumulative performance of suppliers is constantly updated automatically according to past practice. This means that the knowledge of suppliers can be retained, categorized, retrieved and managed effectively.     

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.     

基于CBR的快速路VMS的信息发布策略

将案例推理技术引入到快速路上可变信息牌的信息发布策略中,利用以往具体案例的经验来解决当前新的信息发布问题.首先对北京市环路上可变信息牌的使用进行了评述,在此基础上提出了基于案例推理的快速路可变信息牌的信息发布策略.详细讨论了基于决策树的案例组织方法和基于模糊推理的案例修正方法.基于案例推理的信息发布策略能够及时准确地把相关信息发布给驾驶员,从而提高出行效率.     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

Development of a decision making system for selection of dental implant abutments based on the fuzzy cognitive map

Dental implant is a medical operation used to restore the functions of damaged or missing teeth. Correct implantation requires the proper selection of size and shape among the implant structures. In this paper, we propose a method of constructing a Web-based decision making system that enables the selection of a suitable type of abutment by taking into account the patient’s anatomical data and preferences that are based on an expert’s knowledge and experience for those patients. After the classification of the types of abutment that can be connected to fixtures of implants, we built a knowledge base and case base library according to the characteristics of osseous tissue and teeth shape to select optimal abutment. Moreover, we introduce a fuzzy cognitive map that takes into consideration expert’s knowledge for factors that affect implantation. After the determination of the cause-and-effect relationship among the concepts of the fuzzy cognitive map, an osseointegration factor with the highest conceptual concentration weight is inferred from the decision making system. In addition, the selection process for abutment is expressed as a decision making tree and then, it is applied for the rule-based reasoning and case-based reasoning. The optimized selection result is finally extracted based on the fuzzy membership function using fuzzy inference.     

基于知识支持的建筑施工质量控制系统研究

将知识管理的理念应用到施工质量控制中,加速施工质量控制中知识的循环过程,提高工程质量预测控制和事故诊断能力。通过湖北省建筑工程质量控制研究实践,开发了集建筑质量控制知识管理和决策支持功能于一体的施工质量控制和事故处理系统(CQIS),系统将正向规则推理和基于案例的推理(CBR)相结合,通过RBR接口调用RBR系统,提高案例推理质量诊断的能力,实现基于知识的综合推理功能。该系统已经获得科技成果鉴定和湖北省科技推广成果。