一种基于Petri 网推理的实例修改方法

实例修改是CBR 的关键技术,实例在修改过程中具有较强的领域依赖性,与实例修改相关的领域知识通常以规则的形式存储于规则知识库中,而规则知识库缺乏整体的协调和组织,基于规则的推理效率比较低。本文提出了一种基于Petri 网推理的实例修改方法。首先采用Petri 网表示规则,较好地反映知识的条理性和内部逻辑;其次利用Petri 网的数学原理进行推理,克服了规则推理效率低的缺点。并采用电冰箱为应用实例,验证了上述方法的可行性和有效性。     

基于实例推理的冲模智能CAD系统关键技术的研究

本文以三维软件(SolidWorks)为平台,对基于实例推理的智能冲模CAD系统关键技术进行了研究。讨论了冲模建模及参数化实现、实例库的建立、实例的检索和存储、实例的评价和修改的方法。     

智能设计系统中的实例推理应用

该文介绍了实例推理技术在甘蔗收割机智能设计系统(SHIDS)中的应用,包括采用ART网络缩小检索范围,以最近邻法计算相似度提取实例,引进置信度的概念量化实例评价结果,并辅以规则推理完成实例修改等一系列步骤。     

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。     

基于混合推理技术的产品配置设计研究

企业产品开发设计周期直接关系到客户响应速度,对企业的市场竞争力有着关键性的影响。以捅风眼机产品为例,通过基于规则和实例的混合推理的方式,利用产品一组合件-零件三层实例库对产品实例、组合件实例和零件实例分层存储。在已有产品实例的基础上,利用已有设计规则对产品局部进行改进,从而完成二次开发设计工作,从而大大提高工作效率。     

基于实例的智能工艺设计系统

针对传统智能工艺设计系统的缺陷与不足，结合基于实例推理（Case-Based Reasoning,CBR）和基于规则推理（Rule-Based Reasoning,RBR)的方法，设计了一个基于实例的智能工艺设计系统，给出了工艺实例一个完整清晰的形式化描述，阐述了新零件与实例进行比较和匹配的策略和算法，在检索出相符的实例后，调用RBR方法对实例进行修正，最终完成复杂的工艺设计任务。     

PDM环境下基于实例推理的变型设计技术研究

针对建立实例库时存在的问题,对基于实例推理的变型设计进行了研究,提出了在PDM环境下建立实例库的方法.分析了实例推理的一般过程,提出了一种基于实例推理的变型设计求解模型,在此基础上, 以汽车产品为例,提出了一种变型设计的检索方法,为实现汽车产品的变型设计奠定了基础.     

基于实例推理的模具设计技术研究

模具设计需要大量借鉴以往的设计方案。对设计经验的合理组织和重用可以缩短模具设计周期,提高模具设计效率。文章介绍了一种基于实例推理技术的模具设计方法:通过状态空间法表示了设计实例;阐述了基于相似度理论的最近邻居算法检索策略,能够对实例库中的实例进行检索。并以凸模零件为例对所讲述的方法及技术进行了说明,结果表明实例推理技术的应用可以提高模具的设计效率。     

基于本体的分布式实例推理技术研究

为了克服单一实例库知识的局限性,实现分布式环境下多数据源的知识重用和共享,提出了一个分布式实例推理系统框架.系统通过本体服务器建立和维护实例库之间的本体知识,其中基本本体为知识的表示提供了全局约束和基础,实例推理服务器可以在基本本体框架下定义领域本体来灵活表达各自的领域知识,而本体目录则为知识的检索提供了向导.本体的引入解决了不同实例库之间知识的互理解和互操作性,能够有效地实现多实例库的协同推理.系统采用Web Service技术构建,是一个开放的系统框架,具有很强的可扩展性.     

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。     

Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.     

The development and utilization of the case-based help-desk support system HOMER

Current case-based reasoning (CBR) process models present CBR as a low-maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, utilization and maintenance have to be put in place. In this paper the shortcomings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems are described.     

基于范例和规则相结合的推理技术

机器学习人员多年来提出诸多机器学习的混合体系结构，以改进机器学习的性能。本文着重提出一个基于范例推理与规则推理相结合的推理技术，以及一个范例库划分算法，其目的是充分发挥两种推理的优势，提高问题求解的效率。最后给出了一些测试结果和相关的结论。     

基于案例推理的软测量方法及在磨矿过程中的应用

针对复杂工业过程中一些关键工艺参数难以用仪表进行在线检测的问题,提出了基于案例推理的软测量方法.案例表示由案例产生时间、工况描述、解及相似度组成;案例检索采用具有多相似度阈值计算的最近相邻策略;案例重用采用基于静态相似度阈值和基于动态相似度阈值两种算法,并给出了新的案例修正和存储策略.用该方法建立的磨矿粒度软测量模型已成功应用在某选矿厂磨矿过程中,应用结果表明提出的方法效果显著,具有推广应用前景.     

基于特征加权C均值聚类算法的案例索引和检索

一个成功的案例推理系统高度取决于如何设计出一个精确并且高效的案例检索机制。提出用特征加权C均值聚类算法（WF—C—means）把源案例中的初始案例分成几类。在WF—C—means的分类结果基础上提出了案例索引方案。实验表明,研究的结果对于一个现实的案例推理系统非常有用。     

CBR中案例相似性测度研究

比较分析几种常用的CBR(case-based reasoning)案例相似性测度,根据具体的CBR案例库提出一种异类权重敏感测度方法(NHWSM),且通过交叉验证的实验方法对所分析的测度进行测试.结果表明,NHWSM相似性测度的性能优于其它几种测度.     

Prototypical case mining from biomedical literature for bootstrapping a case base

This article addresses the task of mining for cases from biomedical literature to automatically build an initial case base for a case-based reasoning (CBR) system. This research takes place within the Mémoire project, which has for goal to provide a framework to facilitate building CBR systems in biology and medicine. By analyzing medical literature, the ProCaseMiner system mines for medical concepts such as diseases, signs and symptoms, laboratory tests, and treatment plans in relationship with one another, and connects them together in a given medical domain. It then organizes these concepts in a higher-level structure called a case. This case mining component provides a definite help to bootstrap the creation of a biomedical CBR system case base, composed of both concrete cases and prototypical cases. Currently, most cases learnt correspond to prototypical cases, given the level of abstraction of their features. This article validates the approach by presenting a comparison between the prototypical cases learnt from stem-cell transplantation domain with those created by a team of experts in the domain.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

Representing and matching simulation cases: A case-based reasoning approach

Simulation modelling is a complex decision-making process that involves the processing of various knowledge and information within a context defined by specific application. Building a “good” simulation model has been heavily reliant on the skill and experience of human expert, which has become one of the most expensive and limited resources in market competition. Case-based reasoning (CBR) can be used to effectively solve problems in ill-defined domains where operations specific knowledge and information are processed in a contextual manner such as simulation modeling. This paper addresses some of the basic issues in applying CBR to improve simulation modeling, with emphasis on knowledge or case representation, case indexing, and case matching. Numerical examples and experimental studies were conducted to verify and validate the concepts and model/algorithms developed. The results showed the effectiveness and applicability of proposed method.