Abstract. An important step in the solution of a target problem in case-based reasoning (CBR) is the retrieval of similar previous cases that can be used to solve the target problem. We review a selection of papers from the CBR literature on aspects of retrieval, such as approaches to the assessment of surface and structural similarity and techniques for automating the construction and maintenance of similarity measures. We also examine a number of retrieval techniques that have been developed to address the limitations of retrieval based purely on similarity. 1

Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered.

Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered.

Abstract. Instilling confidence in the abilities of machine learning systems in end-users is seen as critical to their success in real world problems. One way in which this can be achieved is by providing users with interpretable explanations of the system’s predictions. CBR systems have long been understood to have an inherent transparency that has particular advantages for explanations compared with other machine learning techniques. However simply suppling the most similar case is often not enough. In this paper we present a framework for providing interpretable explanations of CBR systems which includes dynamically created discursive texts explaining the feature-value relationships and a measure of confidence of the CBR systems prediction being correct. We also present the results of a preliminary user evaluation we have carried out on the framework.It is clear from this evaluation that being right is important. It appears that caveats and notes of caution when the system is uncertain damage user confidence. 1

Abstract. The information extraction system iDocument interactively extracts information from text such as instances and relations with respect to existing background knowledge. An extraction process creates weighted recommendations describing indications of relevant information. During execution, each process step records its output into an instantiated process model. We reused these bits of information for generating conceptual, functional as well as causal explanations. The purpose of these explanations is to illustrate the evolution of recommendations for convincing users of their validity. In order to visualise explanations, our component utilises different mechanisms for textual, tabular, and graphical rendering styles.