FGCBR 2027 Submission Information

1st French-German Workshop on Case-Based Reasoning

The FGCBR Workshop brings together CBR researchers, particularly from France and Germany. This first edition focuses on adaptation in CBR. To take part, submit a one-page abstract of what you would like to present.

Date
February 11, 2027
Venue
LORIA, Université de LorraineNancy, France
How to Submit Read the Call for Presentations
Hosted by LORIA Université de Lorraine

Overview

The French-German Workshop on Case-Based Reasoning (FGCBR) brings together researchers working on case-based reasoning (CBR), particularly from France and Germany. The first edition focuses on adaptation in CBR: how the solutions of retrieved cases are adapted to new problems, how adaptation knowledge is acquired or learned, and how adaptation can be combined with other AI methods. Contributions on other CBR topics will also be considered.

The workshop will take place at LORIA, Université de Lorraine, in Nancy, France, on February 11, 2027. Instead of full papers, participants submit a one-page abstract that describes what they would like to present. Finished or published work, work in progress, system demos, and open questions all fit the format. After the workshop, the abstracts will be published together on HAL.

Call for Presentations

Submit a one-page abstract describing what you would like to present.

We invite presentations on research, ongoing work, applications and systems in case-based reasoning. A presentation can build on published work, report early results or raise open questions for discussion.

Focus of This Edition: Formalizing Adaptation in CBR

This edition focuses on how adaptation in CBR can be formalized, especially when the objects to be adapted are workflows, as in Process-Oriented Case-Based Reasoning (POCBR). Over the years, research groups have developed many adaptation approaches, often independently of each other and tailored to specific domains or case representations.

We are particularly interested in unifying approaches that cover and go beyond these individual methods. A common formalization helps to understand the underlying principles of adaptation in CBR, supports the development of efficient, high-quality and domain-independent adaptation methods, and helps to explain the adaptation process more clearly to users.

Topics of interest include (but are not limited to):

Formalizing Adaptation

  • Formal Models and Frameworks for Adaptation in CBR
  • Unifying Views on Existing Adaptation Approaches (e.g., Rule-Based, Transformational, Derivational, or Compositional Adaptation)
  • Domain-Independent and Reusable Adaptation Methods

Adaptation of Workflows and Structured Cases

  • Adaptation of Workflows and Processes in Process-Oriented CBR (POCBR)
  • Adaptation of Other Structured Cases (e.g., Graphs, Plans, Texts, or Time Series)

Adaptation Knowledge and Methods

  • Acquisition and Learning of Adaptation Knowledge
  • Adaptation with Machine Learning and Large Language Models
  • Efficiency and Quality of Adaptation

Explanation and Evaluation

  • Explaining Adaptations to Users
  • Evaluation of Adaptation Methods

Other CBR Topics

Presentations on the focus topic will be given priority. Depending on the number of available slots, we will also consider presentations on other CBR topics, for example:

  • Case Representation and Knowledge Modeling
  • Similarity Measures and Retrieval
  • Case Base Maintenance and Retention
  • Explainable and Hybrid CBR (e.g., CBR Combined with Machine Learning)
  • Applications and CBR Systems in Practice

Important Dates

  1. One-Page Abstract Submission Deadline
  2. Notification of Acceptance
  3. Registration Deadline
  4. Workshop For authors of accepted abstracts. Registration details will be sent by the organizers.

All deadlines are 23:59 Central European Time (CET, UTC+1).

Submission Details

One page, using our LaTeX template, submitted as PDF.

To apply for a presentation, please submit a one-page abstract that describes the topic of your presentation, its context (e.g., the project or paper it builds on), and what you would like to discuss with the audience. Abstracts must be written in English and follow the LaTeX template (see below). The abstract should be no longer than one page, and must be submitted as a PDF. The submission link will be announced as soon as it is available. If possible, please relate your abstract to the focus of this edition, adaptation in CBR.

Content
The topic of your presentation, its context (for example the project or paper it builds on) and what you would like to discuss with the audience.
Length
One page.
Format
Please use the provided LaTeX template: Open the abstract template in Overleaf. Create your own copy and submit the resulting PDF.
Language
English
Selection
The organizers review and select the presentations. There is no formal review by a program committee.
Publication
After the workshop, all one-page abstracts will be compiled into a joint collection and published on HAL, the open archive for scientific publications.

Workshop Program

The program will be published here once the presentations have been reviewed and selected.

Participation & Venue

Registration
Authors of accepted abstracts will receive detailed registration information from the organizers.
Attendance
If you would like to attend the workshop without presenting, please contact the organizers.
Venue
LORIA, Université de Lorraine
Campus Scientifique
615 Rue du Jardin-Botanique
54506 Vandœuvre-lès-Nancy, France
Schedule
The workshop takes place on February 11, 2027. Start and end times will be announced here.

Organization

Organizers

Lukas Malburg

Dr. Lukas Malburg

Trier University and German Research Center for Artificial Intelligence (DFKI), Germany & Université de Lorraine, CNRS, Inria, LORIA, France

Acknowledgments

FGCBR 2027 is organized as part of the UniGR Guest Professorship of Dr. Lukas Malburg at the Université de Lorraine, which aims to strengthen the collaboration between French and German research groups in case-based reasoning.

University of the Greater Region (UniGR)