@inproceedings{Lenz2024CBRkitIntuitiveCaseBased,
  title = {{{CBRkit}}: {{An Intuitive Case-Based Reasoning Toolkit}} for~{{Python}}},
  shorttitle = {{{CBRkit}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Malburg, Lukas and Bergmann, Ralph},
  editor = {{Recio-Garcia}, Juan A. and {Orozco-del-Castillo}, Mauricio G. and Bridge, Derek},
  year = {2024},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14775},
  pages = {289--304},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-63646-2_19},
  abstract = {Developing Case-Based Reasoning (CBR) applications is a complex and demanding task that requires a lot of experience and a deep understanding of users. Additionally, current CBR frameworks are not as usable as Machine Learning (ML) frameworks that can be deployed with only a few lines of code. To address these problems and allow users to easily build hybrid Artificial Intelligence (AI) systems by combining CBR with techniques such as ML, we present the CBRkit library in this paper. CBRkit is a Python-based framework that provides generic and easily extensible functions to simplify the creation of CBR applications with advanced similarity measures and case representations. The framework is available from GitHub and PyPI under the permissive MIT license. An initial user study indicates that it is easily possible even for non-CBR experts and users who only have limited Python programming skills to develop their own customized CBR application.},
  isbn = {978-3-031-63646-2},
  langid = {english},
  note = {Best Student Paper Award at ICCBR 2024},
  url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2024CBRkitIntuitiveCaseBased.pdf}
}

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