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.     

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.     

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。     

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.     

案例修正方法研究

提出并实现了一种基于广义算子模型的相似案例案修正方法,即用这些广义算子模型转换一个旧案例成为与新案例完全相同的案例,从而得到问题的解。     

CaBMA: a case-based reasoning system for capturing,refining, and reusing project plans

In this paper, we present CaBMA, a prototype of a knowledge-based system designed to assist with project planning tasks using case-based reasoning. CaBMA introduces a novel approach to project planning in that, for the first time, a knowledge layer is added on top of traditional project management software. Project management software provides editing and bookkeeping capabilities. CaBMA enhances these capabilities by automatically capturing project plans in the form of cases, refining these cases over time to avoid potential inconsistency between them, reusing these cases to generate plans for new projects, and indicating possible repairs for project plans when they derive away from existing knowledge. We will give an overview of the system, provide a detailed explanation on each component, and present an empirical study based on synthetic data.     

A model of legal reasoning with cases incorporating theories and values

Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, and the social values which justify those rules. Having given our view of these relationships, we present our formal model of them, and explain how theories can be constructed, compared and evaluated. We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work. We conclude by identifying some directions for future work.     

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。     

An introduction to case-based reasoning

Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Case-based reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.This article is excerpted from Case-Based Reasoning by Janet Kolodner, to be published by Morgan-Kaufmann Publishers, Inc. in 1992.This work was partially funded by darpa under Contract No. F49620-88-C-0058 monitored by AFOSR, by NSF under Grant No. IST-8608362, and by ARI under Contract No. MDA-903-86-C-173.     

基于智能CASE库的多时相SAR影像分类方法—土地利用及变化监测

针对SAR影像分类,提出了一种基于智能案例(CASE)库多时相SAR影像分类方法。该方法主要分为4部分:SAR影像预处理;智能CASE的建构;基于CASE相似度匹配的SAR影像分类;分类后处理。在智能CASE建构期间,引入时空分析技术去除“伪”CASE,从而保证了CASE库中CASE信息的可靠性。接着,在基于CASE匹配的SAR影像分类过程中,采用分层相似度评价的方法,消除CASE特征相互之间的混叠效应。最后,采用面向对象的方法进行影像分类后处理。该方法有效地考虑了分类地块的形状因子,使分类结果更精确、更符合逻辑性。以2000年(4景,包含4个季度)和2004年(3景,包含3个季度)的多时相SAR影像作为实验数据,结果表明,使用我们提出的方法能达到较好的SAR影像分类结果,分类总体精度达到85%～90%,这为利用多时相SAR影像实施土地利用和变化监测(Land Use and Land Cover Change,LULC)奠定了良好基础。     

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。     

A reduction-graph model of precedent in legal analysis

Legal analysis is a task underlying many forms of legal problem solving. In the Anglo-American legal system, legal analysis is based in part on legal precedents, previously decided cases. This paper describes a reduction-graph model of legal precedents that accounts for a key characteristic of legal precedents: a precedent's relevance to subsequent cases is determined by the theory under which the precedent is decided. This paper identifies the implementation requirements for legal analysis using the reduction-graph model of legal precedents and describes GREBE, a program that satisfies these requirements.     

基于规则和事例混合推理的设计型专家系统模型研究

该文根据设计活动的特点，在综述设计事例表示，组织与检索模型基础上详细讨论了基于规则混合推理专家系统结构模型，以及在收音机起落架起落机设计系统ＬＥＤＥＳ上具体应用。     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理(Case-Based Reasoning,简称CBR)的机制进行了介绍,给出了一个应用CBR技术进行异常处理的工作流模型,并研究了应用CBR方法处理工作流异常的关键技术。     

基于案例的动态科学工作流模型

为提高科学工作流对不确定性因素的处理能力,本文建立了一种树状结构的动态科学工作流模型,它通过与基于案例的推理技术相结合,能很好地解决科学工作流对动态性的要求,提高了科学工作流管理系统的自适应性。基于案例推理的重用,为解决科学工作流低重复性问题、实现科学工作流从单个计算步骤到整个流程定义的多层次重用提供了有效的解决手段。     

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.