Earl is a case-based reasoning system which works in the domain of sandwich recipes. It has a case base of 9,507 cases, each being a sandwich recipe which we scraped from the web. We show how Earl can compose new sandwiches from the ingredients of sandwiches that it retrieves from its case base. On each iteration, it inserts into the sandwich the candidate ingredient which results in greatest c...

In this paper we overview the ongoing research into the application of case-based reasoning in emergency management, based on which we propose a new approach for representation of large-scale disaster cases. The approach takes the environmental factors into account, and the case is organized according to key scenes, rather than disaster types. Each scene consists of inherent attributes, which a...

In this paper we offer a formal account of reasoning with legal cases in terms of argumentation schemes. These schemes, and undercutting attacks associated with them, are formalised as defeasible rules of inference within the ASPIC+ framework. We begin by modelling the style of reasoning with cases developed by Aleven and Ashley in the CATO project, which describes cases using factors, and then...

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 sim...

Among the various machine learning techniques, case-based reasoning is particularly suited to design tasks. Designers often develop new products with a case-based approach, i.e. by reusing already known design products and/or processes. In this paper, we present a tool for case-based design, called STEP PC, and elaborate on our experience with the tool to identify issues and lessons learned in ...

The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As...

A system that applies a method of knowledge-intensive case-based reasoning, for repair and prevention of unwanted events in the domain of offshore oil well drilling, has been developed in cooperation with an oil company. From several reoccurring problems during oil well drilling the problem of ”lost circulation”, i.e. loss of circulating drilling fluid into the geological formation, was picked ...

In this paper we describe a case-based reasoning application developed for aiding engineers in the design of chemical absorption plants. Based on the notion of flow sheet, the paper describes how the application uses a highly structured representation of cases and similarity criteria based on chemical knowledge for designing solutions following an interactive case-based reasoning approach.

This paper presents a prototype of case-based reasoning, built for the agricultural domain. Its aim is to forecast the allocation of a new energy crop, the miscanthus. Interviews were conducted with french farmers in order to know how they make their decisions. Based on interview analysis, a case base and a rule base have been formalized, together with similarity and adaptation knowledge. Furth...