Professor Ralph Bergmann, University of Trier & DFKI, Germany
From Case-Based Reasoning to Experience-Grounded Agentic AI: Foundations, Evolutions, and Future Directions
Case-Based Reasoning (CBR) is a methodology for experience-based problem solving. This talk revisits its conceptual roots and outlines its evolution within my research group, with a focus on process-oriented CBR. It presents recent results on leveraging machine learning and deep learning for similarity assessment and case adaptation, including the use of large language models. Furthermore, the talk explores both parallels and differences between CBR and Retrieval-Augmented Generation (RAG), and highlights the limitations of static reasoning pipelines that dominate current approaches. Building on this analysis, the EXAR architecture (Experience-Grounded Agentic Reasoning) is proposed. EXAR redefines experience-based reasoning as a dynamic process among specialized agents operating on a persistent, structured long-term memory. The talk concludes by outlining key research challenges and future directions for experience-based agentic AI.
Professor Claire Gardent, LORIA & CNRS, France
Case-Based Reasoning and Retrieval Augmented Generation
In Natural Language Processing (NLP), Retrieval Augmented Generation (RAG) has been gaining traction as a way to complement the parametric knowledge encoded in Large Language Models (LLMs) with explicit information collected on the fly to expand the input query with relevant knowledge. This talk will start by briefly summarizing the impact of neural approaches on natural language processing (NLP), explaining how these methods have revolutionized the field by addressing some of the key challenges raised by natural language. The presentation will go on to discuss the parallel between RAG approaches, which retrieve and generate, and Case Based Reasoning, which retrieves, reuses, revises and retains. This latter part of the talk will be based on NLP examples Dr. Gardent has been working on with her students: retrieval based question answering and summarizing, generating Wikipedia biographies and verbalizing knowledge graphs.
Professor David Leake, Indiana University, USA
CBR Tomorrow: Cases in the Age of Generative AI
Case-based reasoning research has a long history, starting more than four decades ago, inspired by insights on human memory, reasoning, and learning. Since that time, CBR research has elucidated the principles, methods, and practice of CBR and illuminated rich opportunities
for synergies with other AI approaches. In 2022 artificial intelligence
had a watershed moment: With the release of ChatGPT and subsequent
models, generative AI took the world by storm, capturing public attention,
transforming AI practice, challenging beliefs about the nature of
intelligence, and recasting expectations for future society. What does
generative AI mean for the future of case-based reasoning? To answer,
I first touch on CBR history, revisiting the meaning of CBR through
foundational motivations, tenets, and past insights. I then highlight why
cases remain vital: what CBR can do that large language models cannot,
the roles of CBR separately and in concert with generative AI, and why
CBR-insipred memory matters. From this analysis I propose opportunities
for tomorrow’s CBR.