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Case-Based Reasoning Research and Development: 32nd International Conference, ICCBR 2024, Merida, Mexico, July 14, 2024, Proceedings 2024 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 462 pages, height x width: 235x155 mm, 103 Illustrations, color; 19 Illustrations, black and white; XIII, 462 p. 122 illus., 103 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 14775
  • Izdošanas datums: 22-Jun-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031636457
  • ISBN-13: 9783031636455
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Case-Based Reasoning Research and Development: 32nd International Conference, ICCBR 2024, Merida, Mexico, July 14, 2024, Proceedings 2024 ed.Palielināt
  • Formāts: Paperback / softback, 462 pages, height x width: 235x155 mm, 103 Illustrations, color; 19 Illustrations, black and white; XIII, 462 p. 122 illus., 103 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 14775
  • Izdošanas datums: 22-Jun-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031636457
  • ISBN-13: 9783031636455
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031636455.html
Keywords:
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 14, 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.
.- 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.