Inside a case-based system, a case c is represented by a set of information entities ei E: c = {e1, e2, ..., en}. It is compared with a query q that is as well just a set of information entities. A composite similarity function for q, c ⊆ E determines a numeric value for the degree of similarity SIM(q, c). SIM is computed by means of a local similarity function sim : E × E → R. A very simple ex...

This paper studies the problem of explaining events in stochastic environments. We explore three ideas to address this problem: (1) Using the notion of Stochastic Explanation, which associates with any event a probability distribution over possible plausible explanations for the event. (2) Retaining as cases (event, stochastic explanation) pairs when unprecedented events occur. (3) Learning the...

In this paper, a forecasting system is presented. It predicts the presence of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. CBR systems are designed to generate solutions to a certain problem by analysing historical data where previous solutions are stored. The system explained includes a novel network for data classification and retriev...

As Case-Based Reasoning has matured as a discipline; the need for a standard means of representing case-based knowledge has come to the fore. While proposals exist for representing the vocabulary and the case-base knowledge containers, there are still no proposed standards for representing similarity or adaptation knowledge. In this paper we present extensions for representing similarity knowle...

The development of large-scale case-based reasoning systems has increased the necessity of providing tools for analyzing the case base structure. In this paper we present a hierarchical competence model approach based on the solution qualities. Using this hierarchical approach we propose a new method for visualizing case base competence and understanding the way a CBR system behaves in differen...

Most synthesis strategies for solution synthesis in distributed expert systems (DESs) are developed based on analytic methods or in-ductive methods. A synthesis strategy presented in this paper is developed by case-based reasoning based on an analogical method. In this paper, the basic frame work of the strategy is introduced and preliminary results are demonstrated. Furthermore, this strategy ...

This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As basi...

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 ot...