Scalable Uncertainty Management: 13th International Conference, Sum 2019, Compiègne, France, December 16-18, 2019, Proceedings

ISBN-13: 9783030355135 / Angielski / Miękka / 2019 / 452 str.

Nahla Ben Amor; Benjamin Quost; Martin Theobald

Spis treści

An Experimental Study on the Behaviour of Inconsistency Measures.- Inconsistency Measurement Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study.- The Hidden Elegance of Causal Interaction Models.- Computational Models for Cumulative Prospect Theory: Application to the Knapsack Problem Under Risk.- On a new evidential C-Means algorithm with instance-level constraints.- Hybrid Reasoning on a Bipolar Argumentation Framework.- Active Preference Elicitation by Bayesian Updating on Optimality Polyhedra.- Selecting Relevant Association Rules From Imperfect Data.- Evidential classification of incomplete data via imprecise relabelling: Application to plastic sorting.- An analogical interpolation method for enlarging a training dataset.- Towards a reconciliation between reasoning and learning - A position paper.- CP-nets, π-pref nets, and Pareto dominance.- Measuring Inconsistency through Subformula Forgetting

Explaining Hierarchical Multi-Linear Models.- Assertional Removed Sets Merging of DL-Lite Knowledge Bases.- An Interactive Polyhedral Approach for Multi-Objective Combinatorial Optimization with Incomplete Preference Information.- Open-Mindedness of Gradual Argumentation Semantics.- Approximate Querying on Property Graphs.- Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.- On cautiousness and expressiveness in interval-valued logic.- Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions.- Evidence Propagation and Consensus Formation in Noisy Environments.- Order-Independent Structure Learning of Multivariate Regression Chain Graphs.- l Comparison of analogy-based methods for predicting preferences.- Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework.- ConvNet and Dempster-Shafer Theory for Object Recognition.- On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions.- Efficient Mo ̈bius Transformations and their applications to D-S Theory.- From shallow to deep interactions between knowledge representation, reasoning and machine learning.- Dealing with Continuous Variables in Graphical Models.- Towards Scalable and Robust Sum-Product Networks.- Learning Models over Relational Data:A Brief Tutorial.- Subspace Clustering and Some Soft Variants.- Algebraic Approximations for Weighted Model Counting.