基于规则与基于实例的集成推理研究

基于实例推理（ＣＢＲ）与基于规则推理（ＲＢＲ）在许多决策支持系统中已得到了广泛的应用。如何有效地提高那些既含有演绎信息又含有类比信息的问题求解效率亦是专家系统开发者的研究课题之一。本文分析了ＣＢＲ和ＲＢＲ的优缺点，回顾了这两种推理方式的工作原理。针对ＲＢＲ与ＣＢＲ应用的局限性，本文提出了一种新的推理模式──ＲＢＲ与ＣＢＲ集成推理。这种模式既利用了ＣＢＲ的长处又利用了ＲＢＲ的优点，力图提高对含有不完整领域知识的问题的推理效率。     

基于案例的推理在维修性设计分析中的应用研究

从维修性工程在我国的应用发展入手,提出了基于案例推理（ＣＢＲ）专家系统在维修性设计分析中应用的必要性。阐述了系统应具有的功能结构模型和处理过程模型;系统设计中的一些问题。并用一简单的例子说明ＣＢＲ的应用过程。最后列举了ＣＢＲ在维修领域应用需进一步研究的问题。     

基于实例推理模式关键技术的研究

众所周知，知识获取的瓶颈现象妨碍了专家系统的快速开发，基于实例推理（ＣＢＲ）通过将知识表达为实例克服了这一问题。本文介绍了在设计类专家系统中建立ＣＢＲ模块的一些关键技术，如实例表达、实例的组织与管理以及最佳实例的提取技术等。探讨了在设计类专家系统中实现ＣＢＲ的途径之一。     

一个基于实例推理的专家系统

该文研究了基于实例推理（Ｃａｓｅ－ＢａｓｅｄＲｅａｓｏｎｉｎｇ,简称ＣＢＲ）的机制,重点讨论了其在商业ＭＩＳ框架生成专家系统ＦＧＳＭ中的应用,结合ＦＧＳＭ系统的研制,给出了ＣＢＲ的一般实现过程。     

基于案例推理的金融危机预警支持系统

传统方法与模型预测金融危机有较大的局限性,该文提出用基于案例推理方法预测金融危机的思想,并给出基于案例推理的金融危机预警系统 ＣＢＲＦＣＰＳＳ的原型,研究了 ＣＢＲＦＣＰＳＳ中的关键技术：案例的知识表达、案例检索和案例学习等。文章最后给出了应用原型系统进行金融危机预警的部分研究成果。     

用基于案例的推理技术建立专家系统

本文讲述了应用基于案例的推理技术建立专家系统的方法，它首先建立一个案例库，将以往解决问题的经验以案例的形式存入库中，当遇到新问题时，它从库中查询相关案例，并应用的找出的案例来解决此新问题。ＣＢＲ技术一个突出的优点就是它在知识缺乏的领域能很好地工作。     

一个基于实例推理的专家系统的设计及实现

本文给出了汽车故障诊断专家系统ＡＢＤＥＳＴ的设计方法，介绍了基于实例的推理的方法，以及ＣＢＲ在故障诊断中的应用和实现技术。     

混合案例推理和规则推理的集成推理框架

在智能决策系统（IDSS）获取知识的推理体系中，案例推理和规则推理有着各自的优点，而混合两者的集成推理可以克服两者的缺点，提高系统的效率和综合推理能力。但是集成推理系统缺乏通用性，延长了开发周期，且不利于规则库和案例库的重用。一种可扩充的集成推理框架为了解决上面的问题而被提出，该框架利用智能决策支持语言Knonit的组件性，对不同的集成方式可方便地扩充相应的集成推理方案，从而快速地搭建IDSS应用；同时规则和案例是作为Knonit广义知识元存在，可以在集成推理框架中复用，另一方面，Knonit的动态特性和可扩充性也对案例库和知识库动态的调整和扩充提供了支持。     

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。     

信息系统总体设计中案例推理与规则推理集成方法的研究

通过对信息系统总体方案设计任务特点的分析和对案例推理与规则推理优、缺点的比较，提出一种信息系统总体设计的案例推理与规则推理相结合的集成推理方法，可以提高信息系统总体设计的效率和质量。     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.     

ASHSD-II: A computational model for litigation support

Abstract: In this paper a hybrid knowledge-based system which exploits both rule-based reasoning (RBR) and case-based reasoning (CBR) is presented. The issues of RBR and CBR in general in the context of legal knowledge-based systems and legislation in rule form and previously-decided cases in an interconnected graph form are discussed. It is possible for the user to select either reasoning method (RBR or CBR), or indicate no preference. The rule base of this system consists of two types of rule. The first type of rule determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rule. When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions. In case of the selection of no preference option, the system applies RBR and CBR method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. These ideas have been implemented in a prototype system, known as A dvisory S upport for H ome S ettlement in D ivorce (ASHSD-II).     

Integrating Case-Based and Rule-Based Reasoning to Meet Multiple Design Constraints

Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of "what if" analysis needed for creative design.
The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria.
We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.     

基于案例与规则推理的故障诊断专家系统

设计并实现基于案例的推理(CBR)与基于规则的推理(RBR)的故障旋转机械诊断专家系统。采用CBR与RBR串行方式进行推理,优先通过案例匹配方式寻求诊断结果,在不适用情况下转入通用性规则推理,并将诊断结果反馈给知识库进行优化。应用结果表明,该系统诊断结果与实际相符合,且诊断速度快、针对性强。     

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases

Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.     

Applying RBR and CBR to develop a VR based integrated system for machining fixture design

A fixture is a special tool used to accurately and stably locate the workpiece during machining process. Proper fixture design improves the quality and production of parts and also facilitates the interchangeability of parts that is prevalent in much of modern manufacturing. This study combines the rule-based reasoning (RBR) and case-based reasoning (CBR) method for machining fixture design in a VR based integrated system. In this paper, an approach combines the RBR and fuzzy comprehensive judgment method is proposed for reasoning suitable locating schemes and locating features. Based on the reasoning results, a CBR method for machining fixture design is then presented. This method could help designers, by referencing previous design cases, to make a conceptual fixturing solution quickly. Finally, the implementation of proposed system is outlined and cases study has been used to demonstrate the applicability of the proposed approach.     

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.     

基于案例推理和规则推理的公安突发事件辅助决策算法

为了提高突发事件发生时公安指挥部门处置决策方案的及时性和科学性,本文提出基于案例推理(Case-Based Reasoning, CBR)和规则推理(Rule-Based Reasoning, RBR)的公安突发事件辅助决策算法。算法根据突发事件的级别、类型和突发事件中的具体数据,如伤亡人数等,通过CBR检索出案例库中同级别同类型的最相似案例,再通过RBR对检索案例的结果进行修正优化使之更适用于突发事件的实际情况。最后通过实例成功地验证了该算法。该算法能够为公安应急预案与辅助决策平台的建设提供参考。