Workshops of the 32nd International Conference on Case-Based Reasoning (ICCBR 2024)

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between intelligent agents and case-based reasoning (CBR) stands as a promising interaction, holding the potential to advance both areas. Intelligent agents, designed to cope with limited computational resources, must perceive their environment and make decisions to achieve goals, while CBR techniques are rooted in harnessing sometimes incomplete knowledge of past experiences to reason effectively in complex situations. Both disciplines have progressed in recent years, but there have been few dedicated meetings on their intersection.
This workshop proposal aims to facilitate the cross-pollination of ideas, methodologies, and innovations between these two disciplines. By merging intelligent agents, which typically leverage abstract knowledge representations, with the experience- focused design of the case-based reasoning methodology, this collaborative endeavor seeks to advance the frontiers of AI, fostering more in-depth insights and novel applications.

This workshop will explore opportunities for combining Case-Based Reasoning (CBR) and Large Language Models (LLMs), fostering a deeper understanding of their synergistic potential. This workshop will present an excellent opportunity for researchers and practitioners to discuss and share their insights on this new, exciting and rapidly evolving field of AI. We look forward to your contributions and an engaging and informative workshop.

To address complex socio-ecological challenges, related to this year's ICCBR conference theme, “CBR for Socio-Ecological Welfare”, this workshop will investigate a deeper integration of context and temporal aspects into CBR through case representations that go beyond attribute-value case representations. A strong focus will be on practical applications of CBR that represent novel solutions to real-world challenges. By considering concrete applications of CBR, the effective use of existing knowledge in intelligent systems and thereby successfully overcoming challenges in a socio-ecological context will be emphasized. This workshop contributes to the conference theme by presenting research work based on complex case representations that promote long-term well-being in socio-ecological systems.
The 2nd BEAR workshop discusses research work, CBR applications, and systems where typical attribute-value case representations reach their limits and complex case representations such as object-oriented, textual, graph-structured, time-oriented (time series), hierarchical or hybrid representations need to be used as well as practical applications of such case representations, that imply necessary (research) challenges. In general, these challenges affect all phases of the CBR cycle. For example, the complex case representation impacts the performance of similarity-based retrieval and adaptation. In addition, it affects the use of other AI methods integrated with CBR. In this workshop, participants shall present their research with complex case representations focusing on general challenges and the impact on the CBR phases or on the application of such representations. The workshop aims to foster collaboration and exchange of ideas among researchers, developers, and others who use complex case representations that go beyond attribute-value case representations.
The BEAR workshop is planning to discuss research work, CBR applications, and systems where typical attribute-value case representations reach their limits and complex case representations such as object-oriented, textual, graph-structured, time-oriented (time series), hierarchical or hybrid representations need to be used.

The Sixth workshop on XCBR aims to provide a medium of exchange for information about trends, research issues and practical experiences in the use of Case-Based Reasoning (CBR) for the inclusion of explanations to several AI techniques (including CBR itself). CBR provides opportunities for exploiting memory-based techniques to generate these explanations that can be successfully applied to the explanation of emerging AI and machine learning techniques.
The problem of explainability in Artificial Intelligence is not new but the rise of the autonomous intelligent systems has created the necessity to understand how these intelligent systems achieve a solution, make a prediction or a recommendation or reason to support a decision to increase users’ trust in these systems.
For this purpose, the XCBR workshop helps an exchange of ideas and interaction, suited to highlight the main bottlenecks and challenges, as well as the more promising research lines, for CBR research related to the explanation of intelligent systems.