# A Review of the Development and Future Challenges of Case-Based Reasoning
## 5 Summary and Challenge
[PERSON] proposed that the CBR system is a living system, and XAI is a constantly changing research field. The case base must update new interpreters to adapt to new problems and solutions; however, one of the biggest shortcomings of the existing XAI library is the lack of personalized interpretation [137]. [PERSON] et al. [65] made attempts to address these shortcomings, and they have mentioned that this is only the first step in personalized interpretation models. Therefore, it is a long-term development outlook to provide personalized interpretation according to different user intentions and needs. It involves multi-domain content, user data representation, AI black box models, and demand analysis, etc. Once these problems and challenges are solved, it is believed that XCBR will go further in the field of AI. (2) Deep CBR  
In recent years, great success in the field of DL has prompted the CBR community to commit to applying DL technology to CBR. CBR, as a methodology, has a complete set of reasoning processes. These processes always need the support of other algorithms and technologies, and DL technology has unparalleled learning ability for large and complex case data, undoubtedly providing new research directions and technical support for CBR research. Table 3 describes the differences and similarities between the two from data, results, learning methods, and other aspects. [PERSON] proposed that the challenge for the CBR community is to define necessary tasks and integration. Introducing DL components into a CBR system in order to realize CBR function can reduce knowledge burden and improve flexibility, and points to the problems faced by its application in case representation, adaptation, and so on. At the same time, [PERSON] also studied the influence of network architecture on the quality of CBR feature extraction [138], which further promoted the development of DL and CBR. As mentioned in the description above, DL technology has been applied in various steps in CBR systems [74; 118; 130]; however, the application of the DL method in CBR is scattered, which is only reflected in a certain step or in some simple combination with the CBR method. It is a direct application based on representation; that is, only some algorithms in DL are selected to implement a process in CBR. Although this can improve the performance and efficiency of CBR systems to a certain extent, it is only the beginning of deep CBR research. It lacks a more abstract general CBR system based on DL idea, or a high-level framework process to integrate advanced CBR with DL architecture. [PERSON] and [PERSON] [30] advocated using CBR to promote the development and challenges of DL. Can we, in turn, use DL thinking to design new CBR frameworks and methods to achieve the whole process of reasoning, learning, and explanation? Alternatively, how can we realize deep reasoning in CBR? Here, depth refers not only to the comprehensive application of DL technology in CBR, but also to deeper learning and reasoning based on the CBR structure. Researchers at Deakin University proposed the implementation from DL to deep reasoning at the 2021 International Conference on Knowledge Discovery and Data Mining (KDD). The emphasis in this tutorial is still on implementing reasoning in DL. Obviously, \begin{table}
\begin{tabular}{c c} \hline
**Deep Learning** & **CBR** \\ \hline Data, experience, and knowledge are all examples & Cases \\ It is about learning knowledge & It is about learning knowledge \\ General rules and laws are generated & Specific solutions are generated \\ Technology & Methodology \\ Unsupervised learning possible & Unsupervised problem solving cannot be done \\ Eager learners & Lazy learners \\ Results are not precise or certain & Results are not precise or certain \\ \hline \end{tabular}
\end{table} 
Table 3: Deep learning versus CBR. CBR is more advantageous at the reasoning level, and its lazy learning can reduce the burden of learning to a certain extent.