CBR简介

 

在网上找到一篇很有见地（加粗部分）的CBR简介，来自Dr. Thomas Roth-Berghofer（http://www.dfki.de/web/research/km/expertise/research/case-based-reasoning?set_language=en&cl=en），翻译（有原文）如下。

 

Case-Based Reasoning (CBR) is a problem-solving paradigm. Its history dates back to the late 1970s. CBR originated in cognitive science (often ascribed to Roger C. Schank and Janet Kolodner). It can be explained in one simple sentence (also known as the CBR assumption):

 

CBR是一个问题求解范例，其历史可以追溯到20世纪70年代后期，它起源于认知科学（通常归功于Roger C. Schank和Janet Kolodner）。CBR可以用一句话来解释（众所周知的CBR假设）：

 

Similar problems have similar solutions.

 

相似问题具有相似解。

 

The basic idea of this assumption is to solve a current problem by reusing solutions that have been applied to similar problems in the past. Therefore, the current problem has to be compared with problems described in cases. Solutions contained in cases that represent very similar problems are then considered to be candidates for solving the current problem, too.

 

这个假设的基本思想是，通过重复使用应用到过去的类似问题的方案来解决当前问题。因此，当前问题必须和案例中描述的问题进行比较，而包含在表示非常相似的问题的案例中的方案也被当作解决当前问题的候选方案。

 

As human beings we use this problem-solving technique in many situations in our daily routine. Whenever it is easier or more convenient to re-use experience, humans prefer to do that rather than to derive solutions from scratch. The physician tells us that he saw the symptoms before, and one injection was all that was needed to heal that patient. The lawyer remembers a suitable case to defend our cause. And we hopefully remember recipes when we are cooking a new meal.

 

作为人类，我们在日常生活中的很多情形下都会使用这个问题求解技术。无论任何时候，只要是重复使用经验比较容易或者更方便，人们就会这样做，而不是白手起家推导出方案。医生告诉我们他之前看到的症状，治愈那个病人只需要注射一针就可以了。律师想起来一个合适的案例来为我们的原因进行辩护。当我们在做一个新菜时我们希望记得以前的菜谱。

 

Case-Based Reasoning simulates this kind of human problem-solving behaviour. It should be considered whenever it is difficult to formulate domain rules, and when cases are available. It should also be considered when rules can be formulated but require more input information than is typically available, because of incomplete problem specifications or because the knowledge needed is simply not available at problem-solving time. Other indications to use CBR are if general knowledge is not sufficient because of too many exceptions, or when new solutions can be derived from old solutions more easily than from scratch. Many successful commercial applications in these areas have proven the utility of this paradigm.

 

CBR模仿人类的这种问题求解行为。当难以把领域规则形式化且案例可用时，就应该考虑这种方法。当规则可以形式化，但是需要更多的典型可用的输入信息时，由于不完整的问题描述，或者在问题求解时所需知识不可用，也应该考虑这种方法。其他使用CBR的情况还有，如果由于期望过多一般知识不充分，或者新方案从旧方案推导出来比白手起家来做容易等等。在这些领域许多成功的商业软件已经证明了这个范例的效果。

 

In order to enable a computer system to judge the similarity of two problems, CBR systems employ so-called "similarity measures" which represent a mathematical formalization of the very general term "similarity" or "utility". Similarity measures usually do not describe the dependencies between problems and corresponding solutions in detail, but only represent a form of a heuristics. Thus, the selection of really useful cases, and therefore the strict correctness of the output, cannot be guaranteed in general. Nevertheless, by tolerating this inexactness one is able to develop powerful knowledge-based systems with significantly less effort and lower costs as compared to the more traditional AI techniques that rely on a complete and correct domain theory.

 

为了使计算机系统判断两个问题的相似度，CBR系统采用所谓的“相似度量”，它表示通用词汇“相似”或“效用”的数学形式。相似度量通常不详细描述问题和对应方案之间的依赖，仅仅表示启发式经验。因此，真正有用的案例的选择，输出结果的严格正确性，一般都不能保证。但是，忍受了这种不精确，就能开发出强大的基于知识的系统，相比传统的依赖完整和正确的领域理论的AI技术而言，只需要较少的努力和较低的代价。

 

Case-Based Reasoning is not limited to the reuse of experience (e.g., as it is used in help-desk applications), however. CBR is also very successful in electronic commerce scenarios and in product retrieval. Here, similarity measures are used to compare user specifications with product descriptions, bridging the gap between customer demands and product features.

 

但是，CBR不限于经验的重复利用（例如，桌面帮助软件），CBR在电子商务和产品检索方面也非常成功。此时，相似度量用于比较用户描述和产品说明，填平了客户需求和产品特征之间的鸿沟。

 

原帖：http://blog.csdn.net/xifeng_2008/archive/2008/10/11/3055716.aspx