Case-based reasoning (CBR) addresses new problems by remembering and adapting solutions previously used to solve similar problems. Pulled by the increasing number of applications and pushed by a growing interest in memory intensive techniques, research on case-based reasoning appears to be gaining momentum. This special issue on Case Based Reasoning gathers together extended versions of selecte...

This instrumental case study documents a community-based participatory evaluation research (CBPER) project that involved a community partner, two graduate students, a faculty member, and an external funder. It highlights the fact that a participatory evaluation model is a viable way to conduct community-based research (CBR) when a community organization needs to know if the program services the...

While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems: knowledge transfer. The issue we intend to elucidate can be expressed as follows: what knowledge present in a source case is transferred to a target problem...

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How to reuse or adapt past solutions to new problems is one of the least understood problems in case-based reasoning. In this paper we will focus on the problem of how to combine solutions coming from multiple cases in search-based approaches to reuse. For that purpose, we introduce the notion of amalgam. Assuming the solution space can be characterized as a generalization space, an amalgam of ...

The article discusses some applications of fuzzy logic ideas to formalizing of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of CBR systems

We combine simple retrieval with domain-specific validation of retrieved cases to produce a useful practical tool for case-based reasoning. Based on 200 real-world cases, we retrieve between three and six cases over a wide range of new problems. This represents a selectivity ranging from 1.5% to 3%, compared to an average selectivity of only 11% from simple retrieval alone.