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.     

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.     

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。     

实例推理和规则推理在实例修改中的应用

在CBR系统中实例修改是一个关键环节,该文通过分析几种实例修改的方法,提出了将实例推理和规则推理进行整合后引入到实例修改过程中,建立修改规则库来完成实例修改,并就如何建立修改规则库进行了说明,为建立智能化的实例修改提供一种思路。     

A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

Case-Based Reasoning System and Artificial Neural Networks: A Review

In this survey paper, the-state-of-art of the connectionist model (i.e. Artificial Neural Network (ANN)) based methodology for a Case-Based Reasoning (CBR) system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. Several ANN models proposed for constructing a CBR system and for solving some special issues involved in a CBR process are described. The main characteristics of each model are analysed, and the advantages and limitations of different models are compared. Also, future research directions are outlined.     

基于SOA的多CBR系统应用集成研究

针对单一应用的案例推理（CBR）系统在集成基于经验的隐性知识时存在固有局限性,设计出基于面向服务的体系结构（SOA）的多CBR系统应用集成框架和集成系统的平台体系结构。该集成框架和平台体系结构在分析CBR系统演化及应用集成模型库的基础上,结合SOA封装推理模型和推理流程,开发集成系统。该系统在试点应用中,实现企业内外多维隐性知识的集成和共享。     

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.     

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.     

Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system

This study intends to propose a hybrid Case-Based Reasoning (CBR) system with the integration of fuzzy sets theory and Ant System-based Clustering Algorithm (ASCA) in order to enhance the accuracy and speed in case matching. The cases in the case base are fuzzified in advance, and then grouped into several clusters by their own similarity with fuzzified ASCA. When a new case occurs, the system will find the closest group for the new case. Then the new case is matched using the fuzzy matching technique only by cases in the closest group. Through these two steps, if the number of cases is very large for the case base, the searching time will be dramatically saved. In the practical application, there is a diagnostic system for vehicle maintaining and repairing, and the results show a dramatic increase in searching efficiency.     

ActiveCBR: An Agent System That Integrates Case-Based Reasoning and Active Databases

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for problem solving that uses previous similar examples to solve a current problem. Despite its success, most current CBR systems are passive: they require human users to activate them manually and to provide information about the incoming problem explicitly. In this paper, we present an integrated agent system that integrates CBR systems with an active database system. Active databases, with the support of active rules, can perform event detection, condition monitoring, and event handling (action execution) in an automatic manner. The integrated ActiveCBR system consists of two layers. In the lower layer, the active database is rule-driven; in the higher layer, the result of action execution of active rules is transformed into feature–value pairs required by the CBR subsystem. The layered architecture separates CBR from sophisticated rule-based reasoning, and improves the traditional passive CBR system with the active property. The system has both real-time response and is highly exible in knowledge management as well as autonomously in response to events that a passive CBR system cannot handle. We demonstrate the system efficiency and effectiveness through empirical tests. Received 21 April 2000 / Revised 12 June 2000 / Accepted in revised form 14 July 2000     

A recommender mechanism based on case-based reasoning

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.     

CBR技术在临床辅助诊断中的应用研究

CBR是一种利用以前类似的案例(Case)来理解并解决当前问题的技术。文章介绍了CBR的技术特点,并对它在临床辅助诊断中的应用进行了研究,主要针对病例库的组织结构、相似病例的检索算法和症状权重的调整等三方面进行了探讨,并给出了相应的解决方案。     

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。     

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.     

基于范例库推理的软件成本估算模型研究

用传统的经验函数估算软件成本有很多局限性，采用基于范例库推理的估算方法可以很好地弥补其中的问题。讨论了软件成本估算和基于CBR推理研究的特点，从总体上阐述了COSCBR系统结构，描述了系统重要的研究方面：影响软件成本因素；层次推理；COSCRB系统的范例表示方法；相似度的基本计算算法。     

一种实时的智能机器人推理机制

为提高实例推理(CBR)机制的实时性,提出一种智能机器人推理机制。给出CBR推理流程,采用时间限定算法和向量空间法对其进行改进,以加快检索和完成学习,使用模块化部件搭建智能平台。实验结果表明,与CBR相比,该机制在完成相同任务时所使用的时间更少,得到的结果更加合理。     

Understanding Similarity: A Joint Project for Psychology, Case-Based Reasoning, and Law

Case-based Reasoning (CBR) began as a theory of human cognition, but has attracted relatively little direct experimental or theoretical investigation in psychology. However, psychologists have developed a range of instance-based theories of cognition and have extensively studied how similarity to past cases can guide categorization of new cases. This paper considers the relation between CBR and psychological research, focussing on similarity in human and artificial case-based reasoning in law. We argue that CBR, psychology and legal theory have complementary contributions to understanding similarity, and describe what each offers. This allows us to establish criteria for assessing existing CBR systems in law and to establish what we consider to be the crucial goals for further research on similarity, both from a psychological and a CBR perspective.