Case-Based Reasoning Research and Development (eBook, PDF)

33rd International Conference, ICCBR 2025, Biarritz, France, June 30-July 3, 2025, Proceedings
Redaktion: Bichindaritz, Isabelle; López, Beatriz

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 33rd International Conference on Case-Based Reasoning Research and Development, ICCBR 2025, held in Biarritz, France, during June 30-July 3, 2025.

The 30 full papers presented in this volume were carefully reviewed and selected from 81 submissions. The book also contains one invited talk in full-paper lenght. The papers are grouped into the following topical sections: Invited Talk; CBR and Generative AI Synergies; Theoretical or Methodological CBR Research; and Applied CBR Research.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 486
• Erscheinungstermin: 29. Juni 2025
• Englisch
• ISBN-13: 9783031965593
• Artikelnr.: 74759095

Inhaltsangabe

.- Invited Talk.

.- EXAR: A Unified Experience-Grounded Agentic Reasoning Architecture.

.- CBR and Generative AI Synergies.

.- AlignLLM: Alignment-based Evaluation using Ensemble of LLMs-as-Judges for Q&A.

.- Visual Question Answering to Generate Case-Based Explanations for Image Classification.

.- Integrating Case-Based Reasoning with LLM for Expense Fraud Detection.

.- LLM-Driven Case-Base Populating for Structuring and Integrating Restoration Experiences.

.- Explaining Translational Embedding Models in Recommender Systems Using Knowledge Graphs and Language Models.

.- Fuzzy Symbolic Reasoning for few-shot KBQA: A CBR-inspired Generative Approach.

.- Context Driven Multi-Query Resolution using LLM-RAG to support the Revision of Explainability needs.

.- LLsiM: Large Language Models for Similarity Assessment in Case-Based Reasoning.

.- Offline-to-Online: Case-Based Knowledge Distillation with Large Language Models for Reinforcement Learning.

.- Utilizing the Structure of Process Models for Guided Generation of Explanatory Texts.

.- Case-Based Reasoning in Generative Agents: Review and Prospect.

.- Theoretical or Methodological CBR Research.

.- Evaluating Objective Metrics for Time Series Model Explainability.

.- A Knowledge Representation Approach for Reasoning with Adaptation Rules.

.- Efficient Case Retrieval Using Dropout Similarity Highway Multigraphs.

.- Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning.

.- A Framework for Supporting the Iterative Design of CBR Applications.

.- Two-Agent Case-Based Reasoning for Prediction.

.- Towards Non-Programmed Robotic Manipulation of Novel Tasks using GA-driven CBR.

.- Fast Locality Sensitive Hashing with Theoretical Guarantee.

.- Learning CaseØstrem Features with Proxy-Guided Deep Neural Networks.

.- Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning.

.- Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification.

.- Applied CBR Research.

.- CBRinR Multitask Multiomics Case-based Reasoning in Bioinformatics.

.- Case-based Causal Reasoning for Elite Sport Training.

.- Representing expert reasoning experience by process cases Application to the delimitation of mobile genetic elements in bacterial chromosomes.

.- Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach.

.- Analysing the contribution of sequential patterns in CBR for childhood obesity prediction.

.- Case-Based Activity Detection from Segmented Internet of Things Data.

.- Explainable sleep-wake recognition using a twin XCBR system with prototypes to improve retrieval efficiency.

.- Case-Based Reasoning with Diffusion Model for Ransomware Detection.