# A Review of the Development and Future Challenges of Case-Based Reasoning
## 5 Summary and Challenge  
[PERSON] [1] and [PERSON] et al. [15] pointed out that the rise and development of CBR is influenced by cognitive science, analogical reasoning, knowledge-based systems, and uncertainty systems. Thus, this method has the advantage of being interdisciplinary. Moreover, the positive aspects of these factors can be used to improve reasoning. As shown by the above key technologies and application development, the CBR method also has some defects; for example, in the processing of unstructured data, CBR applications are relatively rare. In terms of the accurate prediction of parameters, it only provides approximate answers and cannot be applied to unsupervised learning problems. As [PERSON] stated, CBR, as a methodology, can be a flexible combination of various technologies to achieve problem solving, interpretation, and learning processes, and can be applied to different research areas [10]. In recent years, AI technology represented by DL has made breakthrough progress and has attracted wide attention around the world. The research and application of CBR are also developing. Combined with the development needs of explainable AI (XAI) [130], according to the idea of DL, we believe that CBR may have the following two research directions in future development:  
(1) Explainable CBR (XCBR) Decision Support  
With the great success of methods such as ML and DL in AI, explanation has been identified as a key factor in the adoption of AI systems in a wide range of environments [131]. The XAI study attempts to address several issues related to the growing need for explanation models, such as; how do we design explanation models? How do we assess the resulting explanation? What knowledge do we need to construct explanations? How do we tell AI systems to provide users with suggestions and decisions, etc.? As mentioned at the beginning of this section, CBR can achieve the function of problem explanation and can be applied well in explanation tasks. It has been studied and applied in interactive interpretation and memory-based technology to generate interpretation, such as expert systems and recommendation systems. Therefore, CBR has significant potential and foundation in explaining opaque (i.e., black box) AI methods and establishing interpretable AI systems. In 2022, the theme of the 4 th XCBR workshop in the 30 th ICCBR was case-based reasoning for the explanation of intelligent systems under the aim of XAI. At present, CBR has made many contributions to XAI research, such as image explanation tasks [132], text explanation tasks [133], and neural network-based explanation tasks [134]. However, CBR is only moderately used in XAI or in certain aspects of intelligent systems; therefore, regarding the interpretability of complex AI systems, CBR still has much room for development, such as [135; 136]. On the one hand, the interpretation application of CBR in AI systems is mainly oriented to users and provides decision support for users with interactive interpretation; however, in fact, the object-oriented aspect of a complex system is not only the users, and should also include the system operation and maintenance of staff, technical personnel, and other related personnel. Therefore, the interpretation should vary from person to person, using CBR to achieve diverse interpretation. How CBR achieves different interpretations according to different objects in AI systems will be an interesting and challenging research direction. The diversity of object-oriented interpretation means that XCBR needs to include the interpretation cases of different objects in the case base. This is not a simple case list, and its construction problem needs to be solved. At the same time, when new objects and new cases appear, it means the emergence of new explanations, which update and maintain the case base. On the other hand, how to establish a user-centered personalized interpretation model has always been a problem worthy of study.