The quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge although this kind of systems are widely applied in several real-life contexts. This offered prior knowledge is handled, in our work, in form of pairwise constraints: Regarding cases, Must-Link (ML) affirms that two given problems should have the same solution, and Cannot-Link (CL) informs that two problems cannot have the same solution. These constraints may also regard vocabulary knowledge in such a way that ML is generated when prior knowledge affirm that two given features offer correlated values, therefore, similar information, and CL is built when they provide different information. This paper proposes a new constrained & integrated method, named CIMMEP, encoding Constrained & Integrated Maintaining Method based on Evidential Policies, for maintaining both vocabulary and CB through eliminating redundancy and noisiness. Since CBR systems handle real-world experiences, which are full of uncertainty, CIMMEP manages this imperfection using a powerful tool called the belief function theory.

基于案例的推理（CBR）系统提出的解决方案的质量高度依赖于记录的经验及其描述属性。因此，为了使他们长期提供准确，高效的响应，需要维护案例库（CB）和词汇知识。但是，尽管这种系统广泛应用于几种实际环境中，但是维护操作通常无法利用所提供的领域专家知识。在我们的工作中，这些提供的先验知识是以成对约束的形式进行处理的：关于案例，Must-Link（ML）确认两个给定的问题应具有相同的解决方案，而Cannot-Link（CL）通知两个问题不能具有相同的解决方案。相同的解决方案。这些限制也可能以这样的方式考虑词汇知识：当先验知识确认两个给定特征提供相关值（因此，相似的信息）时生成ML，而当它们提供不同的信息时构建CL。本文提出了一种新的约束和集成方法，称为CIMMEP，编码基于证据策略的约束集成维护方法，用于通过消除冗余和噪声来维护词汇和CB。由于CBR系统处理的是充满不确定性的现实世界经验，因此CIMMEP使用一种称为信念函数理论的强大工具来管理这种缺陷。