Case-Based Reasoning Research and Development : 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings / / edited by Mark T. Keane, Nirmalie Wiratunga

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (420 pages)
• Disciplina: 153.43; 006.333
• Soggetto topico: Artificial intelligence; Information technology - Management; Computer engineering; Computer networks; Artificial Intelligence; Computer Application in Administrative Data Processing; Computer Engineering and Networks
• Persona (resp. second.): KeaneMark T. <1961->; WiratungaNirmalie
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Explainability in CBR Using Case-based Reasoning for Capturing Expert Knowledge on Explanation Methods -- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- How close is too close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- Algorithmic Bias and Fairness in Case-Based Reasoning -- ”Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- Representation and Similarity Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- Exploring the Effect of Recipe Representation on Critique-based Conversational Recommendation -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- Adaptation and Analogical Reasoning Case Adaptation with Neural Networks: Capabilities andLimitations -- A Deep Learning Approach to Solving Morphological Analogies -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- Graphs and Optimisation Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- swarm optimization in small case bases for software effort estimation -- MicroCBR: Case-based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- Never judge a case by its (unreliable) neighbors: Estimating Case Reliability for CBR -- CBR and Neural Networks Improving Automated Hyperparameter Optimization with Case-Based Reasoning -- A factorial study of neural network learning from differences for regression -- ase-Based Inverse Reinforcement Learning Using Temporal Coherence -- Analogy-based post-treatment of CNN image segmentations -- Case-Based Applications An Extended Case-Based Reasoning Approach to Race-Time Prediction in Recreational Marathon Runners -- Forecasting for Sustainable Dairy Produce: Enhanced Long-Term, Milk-Supply Forecasting Using k-NN for Data Augmentation, with Prefactual Explanations for XAI -- A Case-Based Approach for Content Planning in Data-to-Text Generation -- The use of computer-assisted Case-Based Reasoning to support clinical decision-making – a scoping review.
• Sommario/riassunto: This book constitutes the proceedings of the 30th International Conference on Case-Based Reasoning, ICCBR 2022, which took place in Nancy, France, during September 12-15, 2022. The theme of ICCBR 2022 was Global Challenges for CBR aiming to consider how CBR can and might contribute to challenges in sustainability, climate change, and global health. The 26 papers presented in this volume were carefully reviewed and selected from 68 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI).
• Titolo autorizzato: Case-Based Reasoning Research and Development
• ISBN: 3-031-14923-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910586636203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 13405