基于描述逻辑的事例推理综述

基于描述逻辑的事例推理(CBR)是当前CBR研究的热点之一。首先介绍了CBR的起源,然后回顾了基于描述逻辑的CBR的发展历史,接着从4个方面:事例表示与组织、事例检索、事例修正和事例库维护综述了基于描述逻辑的CBR的研究工作,最后指出了基于描述逻辑的CBR目前存在的问题并相应地提出了未来的研究方向。     

基于ALCQ(D)的CBR事例修正算法研究

CBR(基于事例推理)是人工智能领域的一个分支,它克服了知识获取的瓶颈问题,事例修正是CBR的关键步骤。以ALC为代表的描述逻辑已被充分应用到CBR中,但目前在基于描述逻辑的CBR中还没有比较有效的算法来判断检索到的相似事例是否需要修正和如何进行修正。ALCQ(D)是在ALC的基础上引入定性数量约束Q和有型域D得到的。提出的算法用ALCQ(D)概念来描述CBR源事例和目标事例,先假定检索到的相似事例能够解决目标问题,即假定目标事例和相似事例同时满足知识库,但这样可能会与知识库产生冲突;接着使用冲突检测机制来查找相似事例概念描述中导致冲突的概念;最后使用概念替换规则在TBox本体库中检索该概念的最相似概念去替换它自己。研究表明,该算法具有界限性、可靠性和完备性。通过一个实例对其进行检验,结果表明,该算法可以准确修正检索到的相似事例,解决目标问题。     

运用哈密顿图进行基于事例推理中的改写

1 前言基于事例的推理(Case-Based Reasoning,简称CBR)是对相似事例进行类比的人工智能推理方法,其原理是利用已有的事例所蕴含的客观规律,通过类比推理,求得新问题的解。由于CBR不必进行知识的提取,而是通过对蕴含客观规律的具体事例进行相似匹配,提取和再利用,因此,CBR克服了专家系统中进行知识获取的困难(知识获取是目前制约专家系统发展的“瓶颈”),能够方便地进行应用。目     

基于事例推理的技术及其应用前景

文章从基于事例推理(CBR)的研究者及使用者的角度,介绍了目前在CBR中较成熟的技术,重点介绍了事例表示、事例检索、事例修改以及 CBR开发工具等,并分析了其中的不足,指出了CBR的发展前景。     

常识知识在事例修改中的应用

事例改写一直是基于事例推理(Case—Based Reasoning，CBR)方法中的难点之一。用知识的观点来诠释基于事例推理，细分了在CBR中所用到的知识，以银行贷款业务为例探讨了常识知识在事例修改中应用的应用方法——综合得分法，并探讨了相应的常识知识的存储。     

基于知识发现的范例推理系统

1 引言范例推理(Case-Based Reasoning,CBR)是近十几年来人工智能中发展起来的区别于基于规则推理的一种推理模式,它是指借用旧的事例或经验来解决问题、评价解决方案、解释异常情况或理解新情况。CBR兴起的主要原因是传统的基于规则的系统存在诸多的缺点,如:在知识获取问题上存在困难,对于处理过的问题没有记忆而导致推理效率低下,不能有效地处理例外情况,整体性能较为脆弱等等,而CBR恰好能解决以上问题。     

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。     

复合事例推理的方法研究

一、引言大多数推理系统是混合的,这是因为它们包含了各种不同类型的子系统。复合系统可以是包括模拟子系统和数字子系统。对基于知识的系统(KBs)而言,复合系统是包括事例、规则、框架结构、约束满足的系统,或是一个传统KBS和一个神经网子系统的集成系统。本文叙述了基于事例的推理系统和其它推理系统的集成系统结构。基于事例推理(CBR)系统是一个用以前经验解决新问题的推理系统。CBR系统根据新问题的主要特征,从己经构造的、存贮了解决过去问题的正     

基于用户上下文的新闻服务机制研究

在新闻服务中引入用户上下文来实现个性化服务.通过定义用户上下文,利用基于事例的推理(CBR)方法,对基于用户新闻请求生成的问题事例进行推理,找到可用的事例,修改问题事例,经过新闻检索,实现个性化新闻服务,提高内容服务质量,优化检索结果,生成最大程度反映用户需求的新闻结果.     

CBR技术在故障诊断中的改进

该文针对基于事例推理(CBR)方法中相似性度量公式(匹配函数)在故障诊断领域应用中存在的问题进行了研究。提出了事例特征分量距离的分区度量方法,以及局部权系数的神经网络迭代算法,并将其用于K-最近邻算法中,显示了该算法的优越性。     

Deep learning and case-based reasoning for predictive and adaptive traffic emergency management

An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to react to detected and forecasted events. We further develop an adapted Reinforcement Leaning (RL) algorithm in building and enhancing the case bases. The proposed system inherits the advantages of CNN, CBR, and RL, which allow detection, prediction, control, evaluation, and learning in a unified framework. To assess the proposed TSCS, we compare our approach with a set of state-of-art algorithms (e.g., multi-agent preemptive case-based reasoning algorithm and multi-agent preemptive longest queue first—maximal weight matching). The proposed TSCS outperforms the benchmarking algorithms through experiments in various traffic scenarios.

     

范例推理在智能决策系统中的应用研究

范例推理是人工智能中重要的推理方法和机器学习技术,它也是智能系统中实用的技术之一。基于范例的决策是决策者认知心理的决策过程的一个合理描述,它提供了一种实现智能系统及决策的现实环境和技术方法。本文提出了基于范例推理的智能决策技术,给出应用模型,并进行了深入讨论。     

Case learning for CBR-based collision avoidance systems

With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.     

一种用于CBR推理机的案例学习算法研究*

案例学习是CBR(Case-Based Reasoning)推理机的重要环节,但由于案例的多样性以及对领域的依赖性,导致CBR系统中案例自动生成困难的问题。针对这一问题,本文提出将seq2seq(Sequence-to-Sequence)模型用于案例学习,通过seq2seq模型自动生成案例,引入attention机制,提高seq2seq模型生成案例的效果,并利用潜在语义分析LSA(Latent Semantic Analysis)对网络爬取语料库进行筛选,利用过滤后的语料库对模型进行训练,提出一种基于三元组的评估方法,对生成案例进行评估和存储,从而实现CBR推理机的自主学习。最后将改进的案例学习系统应用到实际的智能机器人上进行验证,测试结果表明该方法具有可行性,且能够有效提高机器人的智能性及易用性。     

Tennessee-Eastman过程的学习型案例推理故障诊断方法

为了提高Tennessee-Eastman(TE)过程的故障诊断准确率,本文研究一种学习型伪度量(learning pseudo metric,LPM)代替距离度量的案例检索方法,并建立了TE过程的案例推理(case-based reasoning,CBR)故障诊断模型.首先建立LPM度量准则并对LPM模型进行训练,其次度量目标案例与每一个源案例的相似度,从中检索与目标案例相似的同类案例,再采用多数重用原则从同类案例中决策出目标案例的解,最后通过TE过程的运行数据对该方法的性能进行测试,并与典型的CBR和BP(back-propagation)神经网络和支持向量机等方法进行对比,表明本文方法能有效提高故障诊断准确率,在实际化工过程中具有一定的推广应用价值.     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

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。     

Case-based reasoning and neural network based expert system for personalization

We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains.We propose feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers’ character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.     

案例推理的故障诊断技术研究综述

基于案例推理(case-based reasoning,CBR)的故障诊断作为一种新的智能诊断技术,模拟人类求解问题的思路,通过历史案例发现新问题的解。概述了CBR的理论基础和基本原理;从工作过程和集成框架两个方面综述了CBR故障诊断技术的研究现状,其中工作过程包括案例的表示、检索和重用,以及案例库的维护,集成框架包括CBR与基于规则推理、CBR与人工神经网络以及CBR与多智能体等三种情况;比较了六种故障诊断技术的特点及应用范围;总结了CBR故障诊断技术有待解决的问题。