Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 16th European Conference, ECSQARU 2021, Prague, Czech Republic, September 21–24, ... (Lecture Notes in Artificial Intelligence)

Preface
Organization
Contents
Argumentation and Analogical Reasoning
Analogies Between Sentences: Theoretical Aspects - Preliminary Experiments
	1 Introduction
	2 Related Work
	3 Formal Framework
		3.1 Strong Analogical Proportions
		3.2 Weak Analogical Proportions
		3.3 Analogical Proportions and Implication
		3.4 Analogical Proportions and Sentences
	4 Experimental Context - Evaluation Metrics
		4.1 Datasets
		4.2 Embedding Techniques
		4.3 Classifiers
	5 Results
		5.1 CNN and RF Results for Generated Sentences
		5.2 CNN and RF Results for PDTB Dataset
	6 Candidate Applications
	7 Conclusion
	References
Non-monotonic Explanation Functions
	1 Introduction
	2 Classification Problem
	3 Abductive Explanation Functions
	4 Properties of Explanation Functions
	5 Argument-Based Explanation Function
	6 Related Work
	7 Conclusion
	References
Similarity Measures Based on Compiled Arguments
	1 Introduction
	2 Background
		2.1 Logical Concepts
		2.2 Similarity Measures
	3 Compilation of Arguments
	4 Extended Similarity Measures
	5 Extended Principles
	6 Conclusion
	References
Necessary and Sufficient Explanations for Argumentation-Based Conclusions
	1 Introduction
	2 Related Work
	3 Preliminaries
		3.1 ASPIC+
		3.2 Basic Explanations
	4 Necessity and Sufficiency
		4.1 Necessity and Sufficiency for Acceptance
		4.2 Necessity and Sufficiency for Non-acceptance
		4.3 Necessity, Sufficiency and Minimality
	5 Applying Necessity and Sufficiency
	6 Conclusion
	References
Addressing Popular Concerns Regarding COVID-19 Vaccination with Natural Language Argumentation Dialogues
	1 Introduction
	2 Conceptualising Argumentation
		2.1 Using Arguments and Concerns for a Persuasive Chatbot
		2.2 Using an Argument Graph as Chatbot Knowledge Base
	3 Hypotheses
	4 Chatbot Design
		4.1 Knowledge Base Construction
		4.2 Understanding the User Input
	5 Experiments
	6 Evaluation of the Chatbot
	7 Discussion and Conclusion
	References
Argument Strength in Probabilistic Argumentation Using Confirmation Theory
	1 Introduction
	2 Defeasible Logic
	3 Probabilistic Argumentation
	4 Modelling Normality
	5 Argument Strength
	6 Confirmation Theory
	7 Multiple Defeasible Rules
	8 Discussion
	References
The Degrees of Monotony-Dilemma in Abstract Argumentation
	1 Introduction
	2 Theoretical Preliminaries
	3 Degrees of Monotony
	4 The ``Degrees of Monotony''-Dilemma
	5 Discussion
	6 Conclusion
	References
Constrained Incomplete Argumentation Frameworks
	1 Introduction
	2 Background
		2.1 Dung's Abstract Argumentation
		2.2 Incomplete AFs
	3 Constrained IAFs
		3.1 Constraints on Completions
		3.2 Definition and Expressivity of CIAFs
		3.3 Complexity Issues
	4 CIAFs and Extension Enforcement
		4.1 Expansion-Based Enforcement
		4.2 Enforcement as Credulous Acceptability in CIAFs
	5 Related Work
	6 Conclusion
	References
Complexity of Nonemptiness in Control Argumentation Frameworks
	1 Introduction
	2 Preliminaries
		2.1 Abstract Argumentation Frameworks
		2.2 Control Argumentation Frameworks
		2.3 Some Background on Complexity Theory
	3 Complexity of Nonemptiness in CAFs
	4 Conclusion
	References
Generalizing Complete Semantics to Bipolar Argumentation Frameworks
	1 Introduction
	2 Background
	3 Bi-Complete Semantics
		3.1 Expressiveness
		3.2 Refinements
	4 Related Work
	5 Discussion
	References
Philosophical Reflections on Argument Strength and Gradual Acceptability
	1 Introduction
	2 Background
		2.1 Arguments as Statement or as Inferential Structures
		2.2 Basic Concepts of Argument Structure and Relations
	3 Logical, Dialectical and Rhetorical Argument Strength
	4 Evaluating Semantics and Principles
		4.1 Be Explicit About Which Aspects of Argument Strength Are Modelled
		4.2 Be Explicit About the Interpretation of Arguments and Their Relations
	5 Conclusion
	References
An Abstract Argumentation and Logic Programming Comparison Based on 5-Valued Labellings
	1 Introduction
	2 Preliminaries
		2.1 Abstract Argumentation
		2.2 Logic Programs and Semantics
	3 From Logic Programs to Argumentation Frameworks
		3.1 Step 1: AF Instantiation
		3.2 Step 2: Applying Argumentation Semantics
		3.3 Step 3: Computing Conclusion Labellings
	4 Semantic Correspondences
	5 The L-Stable Semantics for Abstract Argumentation Frameworks
	6 On the Role of Sink Arguments
	7 Conclusion
	References
Assumption-Based Argumentation Is Logic Programming with Projection
	1 Introduction
	2 Formal Preliminaries
		2.1 Normal Logic Programs
		2.2 Assumption-Based Argumentation
	3 From ABA to LP
	4 From LP to ABA
	5 Summary and Main Result
	6 Conclusion
	References
A Paraconsistent Approach to Deal with Epistemic Inconsistencies in Argumentation
	1 Introduction
	2 The ASPIC? Framework
		2.1 Attacks and Defeats
		2.2 Abstract Argumentation Frameworks with Two Kinds of Defeats
	3 Rationality Postulates
	4 Related Work and Discussion
	5 Conclusion and Future Works
	References
Gradual Semantics for Weighted Bipolar SETAFs
	1 Introduction
	2 Formal Setting
	3 Desirable Properties
	4 Links Between Properties
	5 Gradual Semantics
	6 Discussion
	References
Bayesian Networks and Graphical Models
Multi-task Transfer Learning for Bayesian Network Structures
	1 Introduction
	2 Bayesian Network Multi-task Structure Learning
	3 The MT-MMHC Algorithm
		3.1 Overall Process of MT-MMHC
		3.2 The Combined Association Measure
		3.3 MT Greedy Search with CPC Constraints
	4 Experiments
		4.1 Experimental Protocol
		4.2 Empirical Results
	5 Conclusion
	References
Persuasive Contrastive Explanations for Bayesian Networks
	1 Introduction
	2 Persuasive Contrastive Explanations
	3 Explanation Lattice
	4 Computing Sufficient and Counterfactual Explanations
		4.1 Searching the Lattice for Sufficient Explanations
		4.2 Searching the Lattice for Counterfactual Explanations
		4.3 Combining the Search for Explanations
		4.4 Complexity and Further Optimisations
	5 Related Work
	6 Conclusions and Further Research
	References
Explainable AI Using MAP-Independence
	1 Introduction
	2 Preliminaries and Notation
	3 MAP-Independence
		3.1 MAP-independence for Justification and Decision Support
	4 Formal Problem Definition and Results
		4.1 Computational Complexity
		4.2 Algorithm and Algorithmic Complexity
	5 Conclusion and Future Work
	References
Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam
	1 Introduction
	2 Models
	3 Probabilistic Inference
	4 Efficient Inference Method for the Score Computation
	5 Experimental Model Comparisons
	6 Discussion
	References
Fine-Tuning the Odds in Bayesian Networks
	1 Introduction
	2 Parametric Bayesian Networks
	3 Parametric Markov Chains
	4 Analysing Parametric BNs Using pMC Techniques
	5 Experiments
	6 Conclusion
	References
Cautious Classification with Data Missing Not at Random Using Generative Random Forests
	1 Introduction
	2 Probabilistic Circuits and Generative Random Forests
	3 Tractable Conservative Inference
	4 Non-ignorable Training Data
	5 Experiments
	6 Conclusion
	References
Belief Functions
Scoring Rules for Belief Functions and Imprecise Probabilities: A Comparison
	1 Introduction
	2 Dutch Books and Proper Scoring Rules
	3 Background on Uncertainty Measures and Their Geometric Interpretation
	4 Coherence for Belief Functions
	5 Comparing Scoring Rules for Belief Functions and Imprecise Probabilities
		5.1 Scoring Rule for Imprecise Probabilities
		5.2 Distinguishing Belief Functions and Imprecise Probabilities Through Scoring Rules
	6 Conclusion
	References
Comparison of Shades and Hiddenness of Conflict
	1 Introduction
	2 Basic Notions
	3 Hidden Conflict
	4 Shades of Conflict
	5 Comparison of both Approaches
		5.1 Comparison by Examples
		5.2 A Theoretic Comparison
	6 Conclusion
	References
Games of Incomplete Information: A Framework Based on Belief Functions
	1 Introduction
	2 Background and Motivations
		2.1 Dempster-Shafer's Theory of Evidence
		2.2 Decision Making with Belief Functions
		2.3 Game Theory
	3 Credal Games
	4 From Credal Games to Complete Games
		4.1 The Direct Transform
		4.2 The Conditioned Transform
	5 Conclusion
	References
Uncertainty-Aware Resampling Method for Imbalanced Classification Using Evidence Theory
	1 Introduction
	2 Related Work
	3 Theory of Evidence
	4 Uncertainty-Aware Hybrid Re-Sampling Method (UAHS)
		4.1 Creating Soft Labels
		4.2 Cleaning the Majority Class
		4.3 Applying Selective Minority Oversampling
	5 Experimental Study
		5.1 Setup
		5.2 Results Discussion
	6 Conclusion
	References
Approximations of Belief Functions Using Compositional Models
	1 Motivation
	2 Belief Functions
	3 Compositional Models
	4 Entropy
	5 Example
	6 Comparison of Heuristics on Random Models
	7 Conclusions
	References
Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching
	1 Introduction
	2 Preliminaries
	3 Statistical Matching
	4 Dempster-Shafer Inner and Outer Approximations
		4.1 Inner Approximating Conditional Belief Functions
		4.2 Outer Approximating Conditional Belief Functions
		4.3 -Contamination Models
	5 Dempster-Shafer Approximation and Inference
	6 Conclusions and Future Works
	References
The Vehicle Routing Problem with Time Windows and Evidential Service and Travel Times: A Recourse Model
	1 Introduction
	2 Definitions and Notations
		2.1 Problem Formulation
		2.2 Belief Function Theory
	3 A Recourse Model for the VRPTW with Evidential Times
		3.1 Formalization
		3.2 Evidential Failures
		3.3 Particular Cases
	4 Experimental Results
	5 Conclusions
	References
Imprecise Probability
A New Score for Adaptive Tests in Bayesian and Credal Networks
	1 Introduction
	2 Related Work
	3 Background on Bayesian and Credal Networks
		3.1 Bayesian Networks
		3.2 Credal Sets and Credal Networks
	4 Testing Algorithms
	5 Adaptive Testing in Bayesian and Credal Networks
	6 A New Score for Testing Algorithms
	7 Experiments
		7.1 Single-Skill Experiments on Synthetic Data
		7.2 Multi-skill Experiments on Real Data
	8 Outlooks and Conclusions
	References
Multi-label Chaining with Imprecise Probabilities
	1 Problem Setting
		1.1 Notions About Imprecise Probabilities
	2 Multilabel Chaining with Imprecise Probabilities
		2.1 Precise Probabilistic Chaining
		2.2 Imprecise Probabilistic Chaining
	3 Imprecise Chaining with NCC
		3.1 Imprecise Branching
		3.2 Marginalization
	4 Experiments
		4.1 Experimental Results
	5 Conclusions
	References
Centroids of Credal Sets: A Comparative Study
	1 Introduction
	2 Preliminary Concepts
	3 Center Points of a Credal Set
		3.1 The Shapley Value
		3.2 Vertex Centroid
		3.3 Incenter
		3.4 Contraction Centroid
	4 Relationships Between the Centroids
		4.1 Probability Intervals
		4.2 Connections Between the Incenter and the Contraction Centroid
	5 Properties of the Centroids
	6 Centrality Measures
	7 Conclusions
	References
The Smallest Probability Interval a Sequence Is Random for: A Study for Six Types of Randomness
	1 Introduction
	2 Forecasting Systems and Randomness
	3 A Martingale-Theoretic Approach—Betting Strategies
	4 Several Notions of (Imprecise) Randomness
	5 Smallest Interval Forecasts and Randomness
	6 What Do Smallest Interval Forecasts Look Like?
	7 When Do These Smallest Interval Forecasts Coincide?
	8 Conclusions and Future Work
	References
Inconsistency Handling and Preferences
Merging Epistemic States and Manipulation
	1 Introduction
	2 Preliminaries
	3 Epistemic State Merging Operators
	4 On Manipulability for ES Merging Operators
	5 Concrete Examples of Merging Operators
	6 Concluding Remarks
	References
Explanation with the Winter Value: Efficient Computation for Hierarchical Choquet Integrals
	1 Introduction
	2 Background
		2.1 Multi-attribute Preference Model
		2.2 Aggregation Models
		2.3 Shapley and Winter Values
		2.4 Explanation of Hierarchical MCDA
	3 Computation of the Explanation for Hierarchical Choquet Integrals
		3.1 Pruning and Recursive Properties
		3.2 Algorithm for Hierarchical Choquet Integrals
	4 Experimental Analysis of the Computation Time
	5 Related Works
	6 Conclusion
	References
Inducing Inference Relations from Inconsistency Measures
	1 Introduction
	2 Inference Relations from Inconsistency Measures
	3 Structural Properties Checking
	4 KLM Properties Checking
	5 Paraconsistency Checking
	6 Dependencies and Incompatibilities
	7 Conclusion and Future Work
	References
The Degree of Conflict Between Formulas in an Inconsistent Knowledge Base
	1 Introduction
	2 Preliminaries
	3 Degree of Conflict Between Formulas
	4 The Causality-Based Explanation
	5 Conclusion
	References
Possibility Theory and Fuzzy Approaches
Representation of Explanations of Possibilistic Inference Decisions
	1 Introduction
	2 Background
	3 Justifying Inference Results
		3.1 Justifying the Possibility Degree b(x)(u) =
	4 Justification and Unexpectedness
		4.1 Extracting Justifications: R and Rn Functions
		4.2 Extracting Unexpectedness: C and Cn Functions
		4.3 Justification and Unexpectedness of b(x)(u)
	5 Representing Explanations of Possibilistic Inference Decisions
		5.1 Explanation Query
		5.2 Vocabulary Construction
		5.3 Possibilistic Conceptual Graphs
		5.4 Conceptual Graphs Based on the Vocabulary VE
		5.5 Representation of Explanations
	6 Conclusion
	References
Towards a Tesseract of Sugeno Integrals
	1 Introduction
	2 Structures of Opposition
		2.1 Logical View of the Classical Square
		2.2 Modern Square of Opposition
		2.3 Classical and Modern Cubes of Opposition
		2.4 The Tesseract
	3 Cube of Sugeno Integral
		3.1 Sugeno Integrals and Related Integrals
		3.2 In Search of a Modern Cube of Sugeno Integrals
	4 Concluding Remarks
	References
Canonical Extension of Possibility Measures to Boolean Algebras of Conditionals
	1 Introduction
	2 Possibility and Conditional Possibility Measures
	3 A Brief Recap on Boolean Algebras of Conditionals
	4 The Strong Conditional Event Problem for Possibility Measures
	5 Conclusions and Future Work
	References
On the KLM Properties of a Fuzzy DL with Typicality
	1 Introduction
	2 The Description Logic ALC and Fuzzy ALC
	3 Fuzzy ALC with Typicality: ALCFT
	4 KLM Properties of ALCFT
	5 Strengthening ALCFT: A Closure Construction
	6 Conclusions
	References
Probability Logics
Trust Evidence Logic
	1 Introduction
	2 Background and Motivations
	3 Syntax and Semantics of TEL
	4 Modeling Trust in TEL
	5 Soundness and Completeness
		5.1 Soundness
		5.2 Completeness
	6 Conclusion and Future Works
	References
Generalized Rules of Probabilistic Independence
	1 Introduction
	2 Preliminaries
		2.1 Semi-graphoid Independence Relations
		2.2 Multiinformation and Rules of Independence
		2.3 Structural Semi-graphoid Independence Relations
	3 Generalizing Inference Rules
	4 Conclusions and Future Research
	References
Algebras of Sets and Coherent Sets of Gambles
	1 Introduction and Overview
	2 Desirability
	3 Stucture of Questions and Possibilities
	4 Information Algebra of Coherent Sets of Gambles
	5 Atoms and Maximal Coherent Sets of Gambles
	6 Information Algebras Homomorphisms
	7 Set Algebras
	8 Conclusions
	References
A Probabilistic Deontic Logic
	1 Introduction
	2 Syntax and Semantics
	3 Axiomatization
	4 Soundness and Completeness
	5 Decidability
	6 Conclusion
	References
Iterated Conditionals and Characterization of P-Entailment
	1 Introduction
	2 Preliminary Notions and Results
	3 A General Notion of Iterated Conditional
	4 Characterization of P-Entailment in Terms of Iterated Conditionals
	5 Conclusions
	References
A Triple Uniqueness of the Maximum Entropy Approach
	1 Introduction
	2 The Maximum Entropy Approach and Two Modifications
	3 Maximal (Modified) Entropy
	4 Modification Number 3
	5 Conclusions
	References
A Logic and Computation for Popper's Conditional Probabilities
	1 Introduction
	2 Popper Functions
	3 Definability
		3.1 Many-Sorted Monadic Second-Order Logic for Popper Functions
		3.2 Definability in Second-Order Logic
	4 Decidability
		4.1 Translation 1
		4.2 Translation 2
		4.3 Translation 3
	5 Computing Algorithms
		5.1 Algorithms
		5.2 Complexity of Quantifier Elimination in the Algorithms
	6 Conclusion and Future Work
		6.1 Conclusion
		6.2 Future Work
	References
Interpreting Connexive Principles in Coherence-Based Probability Logic
	1 Introduction
	2 Preliminary Notions and Results
	3 Approach 1: Connexive Principles and Default Reasoning
	4 Approach 2: Connexive Principles and Compounds of Conditionals
	5 Concluding Remarks
	References
Correction to: Persuasive Contrastive Explanations for Bayesian Networks
	Correction to: Chapter “Persuasive Contrastive Explanations for Bayesian Networks” in: J. Vejnarová and N. Wilson (Eds.): Symbolic and Quantitative Approaches to Reasoning with Uncertainty, LNAI 12897, https://doi.org/10.1007/978-3-030-86772-0_17
Author Index