Scalable Uncertainty Management: 16th International Conference, SUM 2024, Palermo, Italy, November 2729, 2024, Proceedings [Paperback]

This book constitutes the refereed proceedings of the 16th International Conference on Scalable Uncertainty Management, SUM 2024, held in Palermo, Italy, during November 2729, 2024.

The 28 full and 7 short papers presented in this volume were carefully reviewed and selected from 43 submissions. SUM 2024 solicited three types of paper submissions: Long papers reporting on original research or providing surveys that synthesize current research trends, short papers describing promising work in progress, systems, or positions on
controversial issues, and extended abstracts.

Repair Based Semantics for Querying Inconsistent Data From Databases to Knowledge Bases and Back.- Looking at conditionals within the possibilistic framework.- From de Finettis coherence to new theories for reasoning under uncertainty.- MIP Outer Belief Approximations of Lower Conditional Joint CDFs in Statistical Matching Problems.- Integrating User Preferences into Gradual Bipolar Argumentation for Personalised Decision Support.- Scaling Up Reasoning From Conditional Belief Bases.- On Decisive Revision Operators and Totally Ordered Information.- Judicial Support Tool Finding the k Most Likely Judicial Worlds.- Flexible risk aware sequential decision making.- Entropy and extropy for partial probability assessments on arbitrary families of events.- Robust Discrete Bayesian Classifier under Covariate and Label Noise.- Imprecise Dynamic Value at Risk Induced by a DS Bivariate Random Walk.- Boolean weighting in possibilistic logic.- Possibilistic provenance.- Towards a logical framework for reasoning with stereotypes.- SDF FuzzIA a Fuzzy Ontology based plug in for the intelligent analysis of geothematic data.- On the Completeness and Complexity of Lifted Temporal Inference.- Bel Coalitional Games.- Lifting Partially Observable Stochastic Games.- Inconsistency Measurement in LTLf Based on Minimal Inconsistent Sets and Minimal Correction Setl³l