Scalable Uncertainty Management. 15th International Conference, SUM 2022 Paris, France, October 17–19, 2022 Proceedings

Preface
Organization
Abstracts of Invited Talks
	Cognitive Logics, and the Relevance of Nonmontonic Formal Logics for Human-Centred AI
	Surfing the Waves of Explanation
	Learning Argumentation Frameworks
	Contents
Tutorial Articles
A Glimpse into Statistical Relational AI: The Power of Indistinguishability
	1 Introduction
	2 Probabilistic Relational Models (PRMs)
	3 Using Indistinguishability in Episodic PRMs
		3.1 Lifted Model
		3.2 Complexity, Tractability, Completeness
		3.3 Lifted Evidence
		3.4 Lifted Queries
	4 Keeping Indistinguishability in Sequential PRMs
	5 Decision Making and Indistinguishability
	6 Conclusion
	References
On Incompleteness in Abstract Argumentation: Complexity and Expressiveness
	1 Introduction
	2 Background: Abstract Argumentation Frameworks
	3 Incomplete Argumentation Frameworks
		3.1 Formal Definitions
		3.2 Reasoning with IAFs
	4 Constrained Incomplete Argumentation Frameworks
		4.1 The Disjunction Problem
		4.2 Towards Higher Expressiveness: Rich IAFs
		4.3 Constrained IAFs
		4.4 Complexity
	5 Related Work
	6 Conclusion
	References
Full Papers: Non-classical Reasoning
Towards a Principle-Based Approach for Case-Based Reasoning
	1 Introduction
	2 Background
	3 CBR Basic Assumption
	4 Axioms for CBR
	5 Conclusion
	References
Iterated Conditionals, Trivalent Logics, and Conditional Random Quantities
	1 Introduction
	2 Preliminary Notions and Results
	3 Some Basic Properties and Iterated Conditionals
		3.1 The Iterated Conditional of Calabrese
		3.2 The Iterated Conditional of de Finetti
	4 Iterated Conditionals and Compound Prevision Theorem
	5 Conclusions
	References
Using Atomic Bounds to Get Sub-modular Approximations
	1 Introduction
	2 Set-Functions and Their Use
		2.1 Additive Set-Functions and Expectation
		2.2 Sub-modular Set Functions
	3 Approximating Set-Functions with Atomic Bounds
		3.1 Approximating Sets of Additive Set-Functions
		3.2 Approximating Sub-modular Functions
	4 An Application to Signal Convolution
		4.1 Signal Processing and Additive Set Functions
		4.2 Sampling and Fuzzy Transformation
		4.3 Derivation of a Discrete Signal
	5 Conclusion
	References
Characterizing Multipreference Closure with System W
	1 Introduction
	2 Reasoning with Conditional Beliefs
		2.1 Conditional Logic
		2.2 Preferential Models
	3 System W and its Characterization with Preferential Models
		3.1 Definition of System W
		3.2 A Preferential Model for System W
	4 Definition of MP-closure
	5 A Reconstruction of MP-closure with System W
		5.1 Characterization of MP-closure with Preferential Models
		5.2 Characterization of MP-closure with System W
	6 Conclusions and Further Work
	References
From Forgetting Signature Elements to Forgetting Formulas in Epistemic States
	1 Introduction
	2 Formal Basics
	3 Delgrande's Forgetting and Marginalization
		3.1 Delgrande's General Forgetting Approach
		3.2 Marginalization
	4 Postulates for Forgetting Signatures in Epistemic States
	5 Delgrand's Postulates for Forgetting Formulas
	6 Conclusion
	References
Full Papers: Inconsistency
A Capacity-Based Semantics for Inconsistency-Tolerant Inferences
	1 Introduction
	2 From Epistemic Semantics of Classical Logic to Capacities
	3 An Elementary Inconsistency Handling Approach
	4 Boolean Capacity Logic
	5 Capacity Semantics for Some Known Inconsistency-Tolerant Logics
		5.1 Reasoning with Maximal Consistent Subsets of Formulas
		5.2 Belnap's Approach
		5.3 Priest Logic of Paradox
		5.4 Argumentative Inference
	6 Conclusion
	References
An Approach to Inconsistency-Tolerant Reasoning About Probability Based on Łukasiewicz Logic
	1 Introduction
	2 Łukasiewicz Logic and Rational Pavelka Logic
	3 FP(RPL): A Logic to Reason About Probability as Modal Theories over RPL
	4 Reasoning with Inconsistent Probabilistic Information in FP(RPL)
	5 Related Approaches
	6 Conclusions and Future Work
	References
A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure
	1 Introduction
	2 Preliminaries
		2.1 Inconsistency Measurement
		2.2 Satisfiability Solving
		2.3 Answer Set Programming
	3 An Algorithm for Ic Based on SAT
	4 An Algorithm for Ic Based on ASP
	5 Experimental Analysis
		5.1 Experimental Setup
		5.2 Results
	6 Conclusion
	References
Full Papers: Decision Making and Social Choice
A Non-utilitarian Discrete Choice Model for Preference Aggregation
	1 Introduction
	2 Related Work
	3 Preliminaries
	4 A Non-utilitarian Discrete Choice Model
	5 MLE of the Parameters of the -Wise Young's Model
	6 Algorithms for Determining an MLE
	7 Numerical Tests
	8 Conclusion
	References
Selecting the Most Relevant Elements from a Ranking over Sets
	1 Introduction
	2 Preliminaries
	3 Properties for Coalitional Social Choice Functions
	4 The Lex-Cel Coalitional Social Choice Function
	5 Conclusion and Future Work
	References
Decision Making Under Severe Uncertainty on a Budget
	1 Introduction
	2 Preliminaries and Definitions
	3 Regret-Based Budgeted Decision Rule
		3.1 Definition
		3.2 Example and Computation
		3.3 Weak Consistency of Sk* and DkmML
	4 Metric-Based Budgeted Decision Rule
		4.1 Definition
		4.2 Example and Computation
		4.3 On Some Properties of DkgMS
	5 First Experimentation
	6 Discussion and Conclusion
	References
An Improvement of Random Node Generator for the Uniform Generation of Capacities
	1 Introduction
	2 Random Node Generator Based on Beta Distribution
		2.1 Background
		2.2 Theoretical Distribution of
		2.3 The Improved Random Node Generator
	3 Experimental Results
	4 Concluding Remarks
	References
Full Papers: Learning
Logical Proportions-Related Classification Methods Beyond Analogy
	1 Introduction
	2 Differences and Similarities in Classification
		2.1 Exploiting Differences and Bongard Problems
		2.2 Using Triplets of Similar Items
	3 Link with (ana)logical Proportions
	4 Algorithms
		4.1 Algorithm 1 Based on Pairs
		4.2 Algorithm 2: Triplets-based Algorithm
		4.3 Baseline Analogical Classifier
	5 Experimentations
	6 Conclusion
	References
Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble
	1 Introduction
	2 Resampling and Ensemble Methods for Imbalanced Classification
		2.1 Resampling
		2.2 Ensemble Learning in Imbalanced Classification
	3 Evidence Theory
	4 Evidential Undersampling-Based Ensemble Learning
		4.1 Evidential Label Assignment
		4.2 Undersampling
		4.3 Base Classifier Learning and Combination
	5 Experimental Study
		5.1 Setup
		5.2 Results and Discussion
	6 Conclusion
	References
Non-specificity-based Supervised Discretization for Possibilistic Classification
	1 Introduction
	2 Background
		2.1 Possibility Theory
		2.2 Discretization of Continuous Features
	3 The Non-specificity Based Discretization Algorithm
		3.1 The Proposed Algorithm
		3.2 Algorithm Steps Discussion
	4 Experimental Study
		4.1 Datasets
		4.2 Possibilistic Dataset Generation
		4.3 Experimental Results and Analyses
	5 Conclusion
	References
Levelwise Data Disambiguation by Cautious Superset Classification
	1 Introduction
	2 Data Disambiguation by Optimistic Superset Learning
	3 Narrowing Down Supersets
	4 Resolving Ties by Twisted Tuning of SVMs
	5 Applications to Undecided Voters
		5.1 Clustering
		5.2 Simulations
		5.3 German Pre-Election Polls
	6 Discussion
	References
Full Papers: Explanation
Descriptive Accuracy in Explanations: The Case of Probabilistic Classifiers
	1 Introduction
	2 Related Work
	3 Preliminaries
	4 Formalising Descriptive Accuracy
		4.1 Unipolar Explanations and Naive DA
		4.2 Bipolar Explanations and Dialectical DA
		4.3 Relational Unipolar Explanations and Naive DA
		4.4 Relational Bipolar Explanations and Dialectical DA
		4.5 Relational Bipolar Explanations and Structural DA
	5 Achieving DA in Practice
	6 Empirical Evaluation
	7 Discussion and Conclusions
	References
Explanation of Pseudo-Boolean Functions Using Cooperative Game Theory and Prime Implicants
	1 Introduction
		1.1 Related Works
		1.2 Contribution
	2 Problem at Stake and Preliminaries
		2.1 Explanation Through Binarization of the Feature Space
		2.2 Feature Attribution Techniques Based on Cooperative Game Theory
		2.3 Prime Implicants
	3 Global Explanation for a Pseudo-Boolean Function
		3.1 Motivating Example and Proposal on Boolean Functions
		3.2 Proposal on PBF
		3.3 Some Properties of Ii
		3.4 Identification of the Subsets Realizing the Maximum Ii
	4 Conclusion and Future Works
	References
Using Analogical Proportions for Explanations
	1 Introduction
	2 Analogical Proportions
	3 Explanation Power of APs
	4 Experiments
		4.1 Attribute Relevance
		4.2 Adverse Example-Based Explanations
		4.3 Examples
	5 Related Work and Prospects for Further Development
	6 Concluding Remarks
	References
Short Papers: Non-classical Reasoning
Towards a Unified View on Logics for Uncertainty
	1 Introduction
	2 Modal Logics and Uncertainty Measures
		2.1 Logical Preliminaries
		2.2 Uncertainty Measures
	3 A Unified Logic for Uncertainty
	4 Future Work
	References
Extending the Macsum Aggregation to Interval-Valued Inputs
	1 Introduction
	2 Background
	3 The Macsum Aggregation
	4 Disjunctive Extension to Interval-Valued Inputs
	5 Conjunctive Extension to Interval-Valued Inputs
	6 Example
	7 Discussion
	References
Short Papers: Explanation
Analogical Proportions, Multivalued Dependencies and Explanations
	1 Introduction
	2 Analogical Proportions
	3 Multivalued Dependencies
	4 Analogical Proportion and Multivalued Dependency: the Link
	5 Explanations and Fairness
	6 Concluding Remarks
	References
Explaining Robust Classification Through Prime Implicants
	1 Introduction
	2 Setting and General Problem Formulation
		2.1 Robust Classification: Setting
		2.2 Explaining Robust Classification Through Prime Implicants
	3 The Case of the Naive Credal Classifier
		3.1 Generic Case
		3.2 Illustrative Case
	4 Conclusion
	References
Author Index