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Case-Based Reasoning Research and Development de Juan A. Recio-Garcia

Case-Based Reasoning Research and Development: Lecture Notes in Computer Science, cartea 14775

Editat de Juan A. Recio-Garcia, Mauricio G. Orozco-del-Castillo, Derek Bridge
en Limba Engleză Paperback – 22 iun 2024
This book constitutes the refereed proceedings of the 32nd International Conference on Case-Based Reasoning Research and Development, ICCBR 2024, held in Merida, Mexico, during July 1–4, 2024.
The 29 full papers included in this book were carefully reviewed and selected from 91 submissions. They cover a wide range of CBR topics of interest both to practitioners and researchers, including: improvements to the CBR methodology itself: case representation, similarity, retrieval, adaptation, etc.; synergies with other Artificial Intelligence topics, such as Explainable AI and Large Language Models; and finally a whole catalog of applications to different domains such as health-care, education, and legislation.
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Specificații

ISBN-13: 9783031636455
ISBN-10: 3031636457
Pagini: 476
Ilustrații: XIII, 462 p. 122 illus., 103 illus. in color.
Dimensiuni: 155 x 235 x 26 mm
Greutate: 0.72 kg
Ediția:2024
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

.- Integrating kNN Retrieval with Inference on Graphical Models in Case-Based Reasoning.
.- Updating Global Similarity Measures in Learning CBR Systems.
.- Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?.
.- Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation.
.- Visualization of similarity models for CBR comprehension and maintenance.
.- Use Case-Specific Reuse of XAI Strategies: Design and Analysis Through An Evaluation Metrics Library.
.- An Empirical Analysis of User Preferences Regarding XAI metrics.
.- CBR-Ren: A Case-Based Reasoning Driven Retriever-Generator Model for Hybrid Long-form Numerical Reasoning.
.- A Case-based Reasoning and Explaining Model for Temporal Point Process.
.- Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach.
.- Ensemble Stacking Case-Based Reasoning for Regression.
.- Retrieval Augmented Generation with LLMs for Explaining Business Process Models.
.- The Intelligent Tutoring System AI-VT with Case-Based Reasoning and Real Time Recommender Models.
.- Explaining Multiple Instances Counterfactually: User Tests of Group-Counterfactuals for XAI.
.- Olaaaf: a General Adaptation Prototype.
.- Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing.
.- Examining the potential of sequence patterns from EEG data as  alternative case representation for seizure detection.
.- Towards a Case-Based Support for Responding Emergency Calls.
.- CBRkit: An Intuitive Case-Based Reasoning Toolkit for Python.
.- Experiential questioning for VQA.
.- Autocompletion of Architectural Spatial Configurations using Case-Based Reasoning, Graph Clustering, and Deep Learning.
.- A Case-Based Reasoning Approach to Post-Injury Training.
.- Towards Network Implementation of CBR: Case Study of a Neural Network K-NN Algorithm.
.- Aligning to Human Decision-Makers in Military Medical Triage.
.- Counterfactual-Based Synthetic Case Generation.
.- On Implementing Case-Based Reasoning with Large Language Models.
.- Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint Training.
.- Item-Specific Similarity Assessments for Explainable Depression Screening.
.- CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering.