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。     

基于事例推理的电路诊断技术研究

本文首先简要介绍了基于事例推理(CBR)和基于规则推理(RBR)的优缺点，其次建立了一个CBR和RBR相结合的电路故障诊断系统，最后说明了该系统的基本结构及设计过程。     

某型飞机导弹发射通道故障诊断专家系统设计

针对某型飞机导弹发射通道故障定位周期长和排除效率低的问题,综合应用故障树分析法和专家系统分析法,设计并开发了某型飞机导弹发射通道故障诊断专家系统;该系统按照故障树结构建立故障知识库,采用基于规则推理(Rule-Based Reasoning,简称RBR)的推理策略进行故障原因的推理与诊断;通过外场和内场使用表明,该系统提高了导弹发射通道的故障诊断效率,减轻了部队维护人员的排故强度。     

轴类件耦合神经网络实例推理CAPP索引模型的研究

本文提出了一种基于神经网络的工艺设计实例推理索引模型。与现存大多数实例推理系统不同该方法用神经网络实现实例的动态分类和索引。实例层次分类的三层结构和基于特殊的聚类模板概念，为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件。     

铁路行车事故救援决策方法研究

传统的铁路行车事故救援多采用人工方式给出救援方案,但事故受多方面因素的影响,救援人员很难及时的给出科学合理的救援方案.针对已有救援知识不完备、不系统的特点,提出规则推理(Rule-based Reasoning,RBR)和案例推理(Case-Based Reasoning,CBR)相结合的两级分层推理框架,给出了系统流程图,说明了RBR与CBR的具体实现方法,并将自组织特征映射网络(Self-Organizing Feature Map,SOFM)应用到事例检索中,有效地提高了检索的效率.仿真实验结果表明系统取得了良好的效果.克服了单一推理的缺点,实现了对救援理论和经验的复用,提高了系统的效率和综合推理能力,并使系统具有了学习能力.研究结果为进一步应用奠定了基础.     

基于神经索引实例与知识推理的混合型智能CAPP策略

提出了一种新的基于耦合神经网络实例与知识的混合推理策略,采用面向对象的方法表达实例和知识,将神经索引模型引入实例推理中,在此基础上实现了ＣＡＰＰ的变异设计。同时,建立了基于零件及其工艺数据知识的分层知识表达与层次式推理机制,从而实现了ＣＡＰＰ的创成式设计。在给出这种知识化的ＣＡＰＰ系统的总体结构之后,详细讨论了基于神经网络的实例索引模型及实例推理和知识基推理的实现过程,由于吸收了派生法的类比设计思     

基于实例的多智能体水平井设计系统模型的研究

针对钻井水平井设计过程涉及的因素繁多,往往是根据以往类似设计问题的经验与结果来求解所面临的问题,论文利用基于实例推理和Agent的概念与技术,将水平井设计实例构造为具有知识、目标和能力的智能实体,提出一个基于实例的多智能体水平井设计模型,利用实例推理和移动计算技术来实现动态水平井设计的多方协作和并行设计。     

一种CBR与RBR相结合的智能家庭推理系统*

介绍了一种CBR与RBR相结合的智能家庭推理系统。将CBR与RBR两种人工智能技术相结合,运用于普适计算的典型应用智能家庭中,首先通过RBR推理出当前用户的活动以及心情等较高级上下文;然后再用CBR进行上下文的再处理,融合多类型或历史的上下文,自动预测相似度最大的上下文,并基于该上下文为用户提供个性化服务。     

基于实例推理的电子工艺规划

本文针对PCB检修工艺中存在的效率低下、标准不统一的问题，提出了一种基于实例推理的工艺规划。在阐述基于实例推理基本原理的基础上，构造了一种推理模型，并讨论了实例匹配算法。实际应用表明，这种设计方案有效地提高了PCB检修工艺的设计效率。     

基于案例与规则推理的干扰查找专家系统

根据无线电干扰查找的特点,将基于案例推理与基于规则推理相结合的机制应用于诊断系统。给出专家系统的整体结构,阐述案例的表示方法和检索方式,介绍专家系统的工作方式。实际应用表明,该系统可以有效提高于扰查找的准确性,有利于提高干扰查找人员的分析和判断能力。     

基于实例推理的智能刺绣编程系统

本文介绍基于实例推理的智能刺绣编程系统。根据电脑刺绣领域问题的需要，建立了描述刺绣样品的实例模型，利用动态存储模型技术实现实例的存储和检索，在此基础上给出了基于实例的推理流程和算法、实例重用和实例保留算法等。基于实例推理方法可大大提高绣品的质量和刺绣编程的效率。     

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。     

案例推理在认知引擎中的应用

遗传算法可用于认知引擎中传输参数的优化,但随着认知用户数的增加,遗传算法染色体增长,导致算法收敛时间过长,难以满足认知无线电实时通信的需求．以现有认知引擎为基础提出一种新型的认知引擎架构,并将案例推理融入遗传算法中,利用案例推理寻找匹配案例,为遗传算法提供初始种群,减小遗传算法选择初始种群的盲目性．仿真分析结果表明,与仅采用遗传算法的认知引擎相比,融合案例推理的遗传算法构造的认知引擎收敛速度和处理能力有显著提高,效用函数值也有一定增强．     

基于事例的推理(CBR)研究综述

基于事例的推理(CBR)作为一种增量式的学习方法,规避了传统人工智能在知识获取上的瓶颈问题,逐渐引起人工智能领域研究者的关注。对基于事例的推理(CBR)现有研究工作进行逻辑上的梳理和系统的总结,有助于今后研究工作的开展,具有深远的理论意义。该文首次提出基于事例的推理(CBR)研究的逻辑体系结构,并在此逻辑分析的基础上,从基本理论、关键技术和实践应用三方面进行了综述,对其中关键、通用的方法和技术进行了比较和评价。最后,对未来的研究方向进行了展望。     

基于不精确信息实例检索模型的研究

传统实例检索模型缺乏对不确定环境中不精确信息的适应性。采用构造因果网络的分阶段实例检索模型,则可以有效地处理实例检索中的不精确性,并能提高基于实例推理系统的性能。     

Case-Based Reasoning system for mathematical modelling options and resolution methods for production scheduling problems: Case representation,acquisition and retrieval

Thanks to a wide and dynamic research community on short term production scheduling, a large number of modelling options and solving methods have been developed in the recent years both in chemical production and manufacturing domains. This trend is expected to grow in the future as the number of publications is constantly increasing because of industrial interest in the current economic context. The frame of this work is the development of a decision-support system to work out an assignment strategy between scheduling problems, mathematical modelling options and appropriate solving methods. The system must answer the question about which model and which solution method should be applied to solve a new scheduling problem in the most convenient way. The decision-support system is to be built on the foundations of Case Based Reasoning (CBR). CBR is based on a data base which encompasses previously successful experiences. The three major contributions of this paper are: (i) the proposition of an extended and a more exhaustive classification and notation scheme in order to obtain an efficient scheduling case representation (based on previous ones), (ii) a method for bibliographic analysis used to perform a deep study to fill the case base on the one hand, and to examine the topics the more or the less examined in the scheduling domain and their evolution over time on the other hand, and (iii) the proposition of criteria to extract relevant past experiences during the retrieval step of the CBR. The capabilities of our decision support system are illustrated through a case study with typical constraints related to process engineering production in beer industry.     

A use of case-based reasoning technique in building expert systems

Matra Marconi Space France and Aramiihs (Action de Recherche et Application Matra Irit en Interaction Homme Système) laboratory have used and evaluated Case Based Reasoning (CBR) techniques in two projects:

• - The first project is about the development of a system dedicated to help satellites AIT/AIV (Assembly Integration and Test/Validation) test engineers to cope with incidents occurring during test activities. The project is funded by the EGSE System Section of ESTEC (European Space Research and Technology Centre.).

• - The second project is related to the building of a knowledge-based system for diagnosis assistance in AIT/AIV activities of Ariane4 Vehicle Equipment Bay (VEB). The project is financed by internal funding of MMS-F.

In the two projects, CBR technique is neither used the same way nor with the same purpose.

In the first project, CBR technique is used to find out or suggest the cause of an anomaly when an incident appears. Confronted with the occurrence of an incident, the system will refer to its characteristics (test context, symptoms…) that are considered as relevant to retrieve previous similar incidents.

In the second project, CBR technique is combined with Rule Based Reasoning and Model Based Reasoning ones to form the reasoning core of a Hybrid Knowledge Based System. When an incident occurs, the system proposes to test engineers a diagnosis approach based on the combination of different knowledge (coded into rule, cases and models).

Aramiihs is a research unit where engineers from MMS and researchers from the IRIT (Institut de Recherche en Informatique de Toulouse) CNRS (Centre National de la Recherche Scientifique) collaborate on problems concerning new types of man-system interaction.     

基于范例的电子产品设计系统

基于范例的推理是人工智能领域应用较广的一种技术。本文成功地将基于范例的推理技术用于电子产品设计系统，并设计了功能模块级和器件级双层推理机制，引入余弦匹配函数。并用实例表明这是一个成功的应用。     

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.