The fifteenth ICCBR Doctoral Consortium (DC) took place June 30-July 1, 2024 in Mérida, Mexico. Since ICCBR 2009, the DC has been an integral part of the annual Case-Based Reasoning conference, inviting Ph.D. candidates to submit their research statements for discussion with senior members of the community. Ph.D. candidates apply to the program by submitting summaries of their doctoral research, including the problems they are addressing, their proposed research plans, and progress made to date. We received and accepted four submissions from three countries. Updated versions of the research summaries are included in this volume. In these, Saugat Aryal discusses his research on establishing a framework for the use of semi-factual explanations in eXplainable AI (XAI), Lawrence Gates on using cases to capture information from multiple sources for object recognition, Alexander Schultheis on his research on hybrid CBR for time series adaptation, and Kaitlynne Wilkerson her research on using CBR to improve the reliability of large language model results. This year, we initiated another DC track, the "thesis pitch program", enabling additional students to participate in DC events and present brief summaries for discussion without a formal written submission. The DC activities commenced on Sunday, June 30th with a two-hour session in which mentees and mentors met to discuss the research statements and make final preparations for the Monday presentations. After an opening talk on Monday, July 1st, the four contributions were presented orally, followed by a discussion led by their respective mentors. This session was followed by a session of thesis pitches and discussion. We would like to express our gratitude to the AI Journal for supporting the DC. With assistance from the ICCBR 2024 organizers, we received funding that enabled us to help defray expenses for DC participants. Additionally, we extend our appreciation to the PC members who provided detailed and valuable feedback on the research statements. We would like to thank the students, mentors, and program committee members for their hard work and dedication in making the DC a resounding success.
Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230–235
Alexander Schultheis LLM Reliability and CBR: How Case-Based Reasoning Can Improve the Performance of Large Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241–245
Kaitlynne Wilkerson