{"product_id":"9783031965586","title":"Case-Based Reasoning Research and Development : 33rd International Conference, ICCBR 2025, Biarritz, France, June 30-July 3, 2025, Proceedings (Lecture Notes in Computer Science 15662) (2025. xiv, 471 S. XIV, 471 p. 115 illus. 235 mm)","description":"\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e.- Invited Talk.\u003c\/p\u003e\u003cp\u003e.- EXAR: A Unified Experience-Grounded Agentic Reasoning Architecture.\u003c\/p\u003e\u003cp\u003e.- CBR and Generative AI Synergies.\u003c\/p\u003e\u003cp\u003e.- AlignLLM: Alignment-based Evaluation using Ensemble of LLMs-as-Judges for Q\u0026amp;A.\u003c\/p\u003e\u003cp\u003e.- Visual Question Answering to Generate Case-Based Explanations for Image Classification.\u003c\/p\u003e\u003cp\u003e.- Integrating Case-Based Reasoning with LLM for Expense Fraud Detection.\u003c\/p\u003e\u003cp\u003e.- LLM-Driven Case-Base Populating for Structuring and Integrating Restoration Experiences.\u003c\/p\u003e\u003cp\u003e.- Explaining Translational Embedding Models in Recommender Systems Using Knowledge Graphs and Language Models.\u003c\/p\u003e\u003cp\u003e.- Fuzzy Symbolic Reasoning for few-shot KBQA: A CBR-inspired Generative Approach.\u003c\/p\u003e\u003cp\u003e.- Context Driven Multi-Query Resolution using LLM-RAG to support the Revision of Explainability needs.\u003c\/p\u003e\u003cp\u003e.- LLsiM: Large Language Models for Similarity Assessment in Case-Based Reasoning.\u003c\/p\u003e\u003cp\u003e.- Offline-to-Online: Case-Based Knowledge Distillation with Large Language Models for Reinforcement Learning.\u003c\/p\u003e\u003cp\u003e.- Utilizing the Structure of Process Models for Guided Generation of Explanatory Texts.\u003c\/p\u003e\u003cp\u003e.- Case-Based Reasoning in Generative Agents: Review and Prospect.\u003c\/p\u003e\u003cp\u003e.- Theoretical or Methodological CBR Research.\u003c\/p\u003e\u003cp\u003e.- Evaluating Objective Metrics for Time Series Model Explainability.\u003c\/p\u003e\u003cp\u003e.- A Knowledge Representation Approach for Reasoning with Adaptation Rules.\u003c\/p\u003e\u003cp\u003e.- Efficient Case Retrieval Using Dropout Similarity Highway Multigraphs.\u003c\/p\u003e\u003cp\u003e.- Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning.\u003c\/p\u003e\u003cp\u003e.- A Framework for Supporting the Iterative Design of CBR Applications.\u003c\/p\u003e\u003cp\u003e.- Two-Agent Case-Based Reasoning for Prediction.\u003c\/p\u003e\u003cp\u003e.- Towards Non-Programmed Robotic Manipulation of Novel Tasks using GA-driven CBR.\u003c\/p\u003e\u003cp\u003e.- Fast Locality Sensitive Hashing with Theoretical Guarantee.\u003c\/p\u003e\u003cp\u003e.- Learning CaseØstrem Features with Proxy-Guided Deep Neural Networks.\u003c\/p\u003e\u003cp\u003e.- Integration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning.\u003c\/p\u003e\u003cp\u003e.- Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification.\u003c\/p\u003e\u003cp\u003e.- Applied CBR Research.\u003c\/p\u003e\u003cp\u003e.- CBRinR Multitask Multiomics Case-based Reasoning in Bioinformatics.\u003c\/p\u003e\u003cp\u003e.- Case-based Causal Reasoning for Elite Sport Training.\u003c\/p\u003e\u003cp\u003e.- Representing expert reasoning experience by process cases  Application to the delimitation of mobile genetic elements in bacterial chromosomes.\u003c\/p\u003e\u003cp\u003e.- Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach.\u003c\/p\u003e\u003cp\u003e.- Analysing the contribution of sequential patterns in CBR for childhood obesity prediction.\u003c\/p\u003e\u003cp\u003e.- Case-Based Activity Detection from Segmented Internet of Things Data.\u003c\/p\u003e\u003cp\u003e.- Explainable sleep-wake recognition using a twin XCBR system with prototypes to improve retrieval efficiency.\u003c\/p\u003e\u003cp\u003e.- Case-Based Reasoning with Diffusion Model for Ransomware Detection.\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":49874408997112,"sku":"00000_00000_00000_00000","price":313.32,"currency_code":"AUD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/8208\/6904\/files\/9783031965586-1.jpg?v=1783773036","url":"https:\/\/kinokuniya.com.au\/products\/9783031965586","provider":"Books Kinokuniya Australia","version":"1.0","type":"link"}