| dc.description.abstract | Over the last years, case-based reasoning has shown to be a promising AI-method in the legal domain,
in which a fortiori reasoning is utilized to find similar cases from the past. However, this approach
limits the decision making process by not being able to make a prediction for every new case, resulting
in a significant number of cases remaining undecided. This thesis discusses the development and
evaluation of a newly created case-based reasoning (C-BR) model designed to address this issue, by
combining two previously designed models: the aforementioned legal C-BR model and a traditional
similarity measure-based C-BR model. Similarity measures are commonly employed in C-BR models
to retrieve previous cases in order to solve new problems. The combined approach proposed in this
case study includes a fortiori reasoning as well, which involves a formal model of legal reasoning
to retrieve similar cases from the past. This combined approach aims to improve the accuracy and
reliability of C-BR models. The combined model was tested on a decision making problem of the
CBR (‘Centraal Bureau Rijvaardigheidsbewijzen’: Dutch Central Office of Driving Certification), in
which a decision of the fitness to drive of individuals was made based on their health deviations. The
performance of the combined C-BR model was compared to models solely utilizing one of the two
approaches. While results indicate that the combination of both techniques is a promising approach
in C-BR, this model does not outperform the more traditional approach yet, making less humanlike decisions than traditional C-BR with a similarity measure. However, this study emphasizes
the necessity of further research into the applicability of the integrated model in other domains.
Future studies could investigate the potential of the combined C-BR model in datasets that are less
complex. This could increase the performance of case-based reasoning models in AI, making these
models applicable in even more domains to assist human decision making. | |
