# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Case Adaptation  
The re-use phase needs to use the experience and knowledge of old cases in new situations and obtain the solution to the new problem by adapting the retrieval results. Figure 5 shows the general re-use principle of a selected case. The general process of this phase is shown in Figure 5. If the new problem is the same as the old case retrieved, then the re-use phase only needs to copy the old case solution; however, in reality, the situation with new problems is often not exactly the same as that of old cases, and the retrievedsolution can only be regarded as an initial reference solution. Any difference between the new problem and the retrieved case may require appropriate adaptation of the solution to adapt to the new problem. Especially when CBR is used for constructive problems, such as design tasks, planning tasks, and decision-making tasks, adaptation is often essential. Because such tasks have difficulty finding every solution in the case base, the adaptation process is essential. The complexity of the adaptation method is reflected in two dimensions: what is changed in the retrieved solution and how the change is achieved [15]. The basic adaptation type is shown in Figure 6, of which the following three types truly need adaptation operations:  
* Substitutions: They replace some part of the retrieved solution by another or by several others. * Structural transformations: They alter the structure of the solution and re-organize the solution by adding, deleting, or replacing parts of the proposed solution [70]. * Generative adaptations: They replay the method of deriving the retrieved solution on the new problem. This is the most complex form of adaptation. The most important part of the adaptation process is the learning of adaptation knowledge, which is also a classic problem in CBR. In the early stage, CBR invested much energy in developing case adaptation knowledge, but the difficulty of knowledge generation seriously hindered the development of CBR. It can also be seen from Figure 3 that there is little previous literature on adaptation. Later in the research process, the potential of learning methods to acquire case adaptation knowledge was gradually recognized, including generating rules through decision tree learning [71, 72] and support vector regression (SVR) [51, 73], and by applying CBR to the adaptation process itself [74, 75]. The case-difference heuristic (CDH) first proposed by [PERSON] and [PERSON] [76] has become one of the most commonly used methods for learning adaptation knowledge. Equation (4) describes the difference between case \(x\) and case \(y\):  
\[\triangle(x,y)=(x_{1}-y_{1},\ldots,x_{k}-y_{k},s_{x}-s_{y})=(\triangle f_{1}, \ldots,\triangle f_{k},\triangle s) \tag{4}\]  
where \(s\) represents the solution for the case, \(\triangle f_{i}\) represents the \(i\)-th feature difference, and \(\triangle s\) represents the scheme difference. CDH is used to attribute the difference in the solution  
Figure 5: Re-use principle. Figure 6: Basic adaptation types. description to the difference in its problem description, transforming the difference into rules, thus achieving the case adaptation process. At present, based on the complexity of case knowledge, methods of learning and adapting knowledge are mainly studied from the following directions:  
1. Improvement of the CDH method [61; 77; 78; 79]. Although the CDH adaptation method reduces the burden of knowledge engineering, it also brings the problem of defining the difference function. Recent studies have used implicit calculation in ML technology to replace the traditional CDH difference calculation, but there is still a lack of theoretical proof. 2. Adaptation method based on knowledge and rules [80; 81; 82]. [PERSON] et al. [81] proposed using positive examples to adapt rules, while using negative examples to filter out some of the rules to avoid the problem of incorrect schemes in CBR systems during the adaptation process.