Scalable Uncertainty Management

Florence Dupin de Saint-Cyr (1, 2) , Meltem Öztürk-Escoffier (3) , Nico Potyka (4)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
2 UT3 - Université Toulouse III - Paul Sabatier
3 Université Paris Dauphine-PSL
4 Imperial College London

This book constitutes the refereed proceedings of the 15th International Conference on Scalable Uncertainty Management, SUM 2022, which was held in Paris, France, in October 2022. The 19 full and 4 short papers presented in this volume were carefully reviewed and selected from 25 submissions. Besides that, the book also contains 3 abstracts of invited talks and 2 tutorial papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. The chapter "Defining and Enforcing Descriptive Accuracy in Explanations: the Case of Probabilistic Classifiers" is licensed under the terms of the Creative Commons Attribution 4.0 International License.

Domaines: Intelligence artificielle [cs.AI]

Florence Dupin de Saint-Cyr, Meltem Öztürk-Escoffier, Nico Potyka. Scalable Uncertainty Management: 15th International Conference, SUM 2022, Paris, France, October 17-19, Proceedings. Lecture Notes in Artificial Intelligence, 13562, Springer Nature, pp.1-371, 2022, Lecture Notes in Computer Science (LNCS), 978-3-031-18842-8. ⟨10.1007/978-3-031-18843-5⟩. ⟨hal-04225974⟩