Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 17th European Conference, ECSQARU 2023, Arras, France, September 19–22, 2023, Proceedings (Lecture Notes in Artificial Intelligence)

This book constitutes the refereed proceedings of the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2023, held in Arras, France, in September 2023. The 35 full papers presented in this volume were carefully reviewed and selected from 46 submissions. The papers are organized in topical sections about Complexity and Database Theory; Formal Concept Analysis: Theoretical Advances; Formal Concept Analysis: Applications; Modelling and Explanation; Semantic Web and Graphs; Posters. Preface Organization Abstracts of Keynote Talks Reasoning about Tree Ensembles Mixing Time and Uncertainty. A Tale of Superpositions On Belief Update According to Katsuno & Mendelzon: Novel Insights Contents Decision Theory Cautious Decision-Making for Tree Ensembles 1 Introduction 2 Preliminaries 2.1 Imprecise Dirichlet Model and Trees 2.2 Belief Functions 2.3 Decision Making with Belief Functions 2.4 Evaluation of Cautious Classifiers 3 Cautious Decision-Making for Tree Ensembles 3.1 Generalization of Averaging 3.2 Generalization of Voting 4 Experiments and Results 4.1 Decision-Making Efficiency 4.2 Cautious Decision-Making Performance Comparison 5 Conclusions and Perspectives References Enhancing Control Room Operator Decision Making: An Application of Dynamic Influence Diagrams in Formaldehyde Manufacturing 1 Introduction 2 Methodology 2.1 Influence Diagram 2.2 Dynamic Influence Diagram 3 Simulation and Validation 3.1 Test Environment/Formaldehyde Production Scenario 3.2 Construction of the Model 3.3 Use of the Model 4 Results and Discussion 4.1 Assessment of DID's Effectiveness in Situational Response 4.2 Limitations and Future Improvements 5 Conclusion References Decision with Belief Functions and Generalized Independence: Two Impossibility Theorems 1 Introduction 2 Background and Notations 3 Toward Impossibility Theorems 4 A Dominance-based Rule for the Comparison of Evidential Lotteries 5 Conclusion References A Logical Framework for User-Feedback Dialogues on Hypotheses in Weighted Abduction 1 Introduction 2 Related Work 3 Hypotheses and Hypothesis Graphs 3.1 Language 3.2 Semantics 4 User-Feedback Dialogue Protocols 4.1 Abstract User-Feedback Dialogue Protocol 4.2 User-Feedback Dialogues on Hypotheses 4.3 User-Feedback Dialogues on Hypothesis Graphs 5 Conclusion and Future Work References Modifications of the Miller Definition of Contrastive (Counterfactual) Explanations 1 Introduction 2 Background 2.1 Structural Equation Models 2.2 Non-contrastive Causes and Explanations 2.3 Contrastive Causes and Explanations 3 Alternative Non-contrastive Explanations 3.1 Original HP vs. Modified HP Definition 3.2 Modified HP vs. Borner Definition 4 Alternative Contrastive Explanations 5 Conclusion References Argumentation Systems Revisiting Approximate Reasoning Based on Grounded Semantics 1 Introduction 2 Background 2.1 Abstract Argumentation 2.2 Harper++ for Approximate Reasoning 3 New Approaches to Acceptability Approximation 3.1 ARIPOTER-degrees: ARgumentatIon ApPrOximaTE Reasoning Using In/Out Degrees of Arguments 3.2 ARIPOTER-hcat: ARgumentatIon ApPrOximaTE Reasoning Using the H-Categorizer Semantics 3.3 Relationships Between Approaches 4 Experimental Settings 4.1 Benchmarks 5 Empirical Analysis 5.1 Solving Time 5.2 Accuracy 6 Related Work 7 Conclusion References Extension-Based Semantics for Incomplete Argumentation Frameworks: Grounded Semantics and Principles 1 Introduction 2 Background 3 Grounded Semantics 4 Principle-Based Analysis of IAF Semantics 5 Related Work 6 Conclusion References An Equivalence Class of Gradual Semantics 1 Introduction 2 Background 3 Equivalence Class of Semantics 4 Another Instance of the Equivalence Class 5 Conclusion References Determining Preferences over Extensions: A Cautious Approach to Preference-Based Argumentation Frameworks 1 Introduction 2 Preference-Based Argumentation Frameworks 3 Extracting Multiple AFs from a Single PAF 4 Preferences over Extensions of a PAF 4.1 Preferences over Extensions When over S(F) It Total 4.2 Preferences over Extensions When over S(F) Is Partial 5 Conclusion References Bayesian Networks A Ring-Based Distributed Algorithm for Learning High-Dimensional Bayesian Networks 1 Introduction 2 Preliminaries 2.1 Bayesian Network 2.2 Structural Learning of BNs 2.3 Bayesian Network Fusion 3 Ring-Based Distributed Learning of BNs 4 Experimental Evaluation 4.1 Algorithms 4.2 Methodology 4.3 Reproducibility 4.4 Results 5 Conclusions References On Identifiability of BN2A Networks 1 Introduction 2 BN2A Models 3 Identifiability of BN2A 4 Computational Experiments 5 Discussion References Normative Monitoring Using Bayesian Networks: Defining a Threshold for Conflict Detection 1 Introduction 2 Preliminaries 3 Measures and Thresholds 3.1 Measures 3.2 Thresholds 4 Bounding IOconfl 5 Choosing a Threshold 6 Experimental Evaluation 6.1 Experimental Set-Up 6.2 Results and Discussion 7 Conclusion and Future Research References An Optimized Quantum Circuit Representation of Bayesian Networks 1 Introduction 2 Basic Quantum Computation 3 Quantum Bayesian Networks 3.1 Classical Bayesian Networks 3.2 Compositional Quantum Bayesian Networks C-QBN 4 Optimized Representation of Quantum Bayesian Networks 5 Experiments 6 Conclusion and Perspectives References A Comparison of Different Marginalization Operations in Simple Propagation 1 Introduction 2 Preliminaries and Notation 3 Probabilistic Inference with Simple Propagation 4 Marginalization Operations During Message Passing 4.1 Variable Elimination 4.2 Arc-Reversal 4.3 Symbolic Probabilistic Inference 5 Experimental Analysis 6 Discussion 7 Conclusion References Non-monotonic Inference and Inconsistency Handling Approximations of System W Between c-Inference, System Z, and Lexicographic Inference 1 Introduction 2 Background: Conditional Logic 3 Combining and Extending Inductive Inference Operators 4 Approximations of System W 5 Conclusions and Future Work References First Steps Towards a Logic of Ordered Pairs 1 Introduction 2 Logical Proportions 3 Elements of a Logic of Ordered Pairs 3.1 Comparing Items in an Ordered Pair 3.2 Combining Relations Between Pairs 3.3 Consequence Relation Between Pairs 3.4 Logical Combinations of Ordered Pairs 3.5 A Creative Inference Process 4 Concluding Remarks References Representing Nonmonotonic Inference Based on c-Representations as an SMT Problem 1 Introduction 2 Background: Conditionals and c-Inference 3 Characterization of C-Inference as an SMT Problem 3.1 SMT with Linear Integer Arithmetic 3.2 Transformation of CR(R) 3.3 Transformation of CR(R,A,B) 4 Maximal Impact Value 5 Implementation and First Evaluation 6 Conclusions and Further Work References On the Cognitive Logic of Human Propositional Reasoning: Merging Ranking Functions 1 Introduction 2 Background on Logic and Ranking Functions 3 Merging Ranking Functions 4 Cognitive Background 5 Sequential Merging Approach 6 Constructing Ranking Functions 7 Experimental Dataset and Modelling 8 Evaluation and Results 9 Conclusions and Future Work References Handling Inconsistency in (Numerical) Preferences Using Possibility Theory 1 Introduction 2 Possibilistic Modelling of Preference Models 2.1 Preferences and Preference Models 2.2 Possibility Theory Reminder 2.3 Possibilistic Preferential Information 2.4 Errors in Set-Wise and Possibilistic Approaches 3 Handling Inconsistencies 3.1 Inferring Despite Inconsistencies 3.2 Resolving Inconsistencies Through Information Fusion 4 Experiments 4.1 Decision Rules 4.2 Experimental Protocol 4.3 Number of Errors Detected 4.4 Uncertainty Management Methods and Decision Rules 5 Conclusion References Learning for Uncertainty Formalisms Evidential Generative Adversarial Networks for Handling Imbalanced Learning 1 Introduction 2 Preliminaries 2.1 Generative Adversarial Networks (GANs) 2.2 Evidential Uncertainty Quantification 3 EvGAN: Evidential Generative Adversarial Networks 3.1 Modified Loss Function 3.2 Networks' Settings 4 Experimental Study 4.1 Experimental Setup 4.2 Results Discussion 5 Conclusion References Learning Sets of Probabilities Through Ensemble Methods 1 Introduction 2 Preliminary 2.1 Probabilistic Classification 2.2 Classification with Set of Probabilities 3 Credal Sets Approximation 3.1 A Quantile-Based Approach 3.2 The Cases of Convex Distances 4 Inference Problem 5 Experiment 6 Conclusion References Neural Graphical Models 1 Introduction 2 Related Works 3 Neural Graphical Models 3.1 Representation 3.2 Learning 3.3 Extension to Generic Data Types 3.4 Inference 4 Experiments 5 Conclusions A Sampling B Design Strategies and Best Practices for NGMs C Modeling Gaussian Graphical Models C.1 Setup C.2 Analysis D Lung Cancer Data Analysis E NGM on Infant Mortality Data (Details) E.1 Representing Categorical Variables E.2 Additional Infant Mortality results References An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models 1 Incremental Elicitation 2 Related Work 3 Problem Setting 4 The Idea Behind Our Approach 5 Query Selection 6 Experimental Results 7 Conclusions and Discussion References PETS: Predicting Efficiently Using Temporal Symmetries in Temporal PGMs 1 Introduction 2 Preliminaries 3 PETS Algorithm: Predicting Efficiently Using Temporal Symmetries 4 Evaluation 5 Conclusion References Reasoning Under Uncertainty Lifting Factor Graphs with Some Unknown Factors 1 Introduction 2 Preliminaries 2.1 Factor Graphs and Parameterised Factor Graphs 2.2 The Colour Passing Algorithm 3 The LIFAGU Algorithm 4 Empirical Evaluation 5 Conclusion References On the Enumeration of Non-dominated Spanning Trees with Imprecise Weights 1 Introduction 2 Notations and Problem Description 2.1 Graph 2.2 Imprecise Weights and Problem Description 2.3 Partial Solution 3 Preliminary Results 4 Enumerating Algorithm 4.1 Possible and Necessary Edges of Partial Solutions 4.2 The Enumerating Algorithm 5 Numerical Experiments 5.1 Instances 5.2 Results 6 Conclusions References A Robust Bayesian Approach for Causal Inference Problems 1 Introduction 2 Causal Estimation 2.1 Regression Model 3 Bayesian Causal Estimation 3.1 Hierarchical Model 3.2 Robust Bayesian Analysis 3.3 Variable Selection and Coefficient Adjustment 4 Simulation Studies 5 Conclusion References Conditional Objects as Possibilistic Variables 1 Introduction 2 Preliminaries 3 Possibilistic Variables and Their Expectations 4 Conditionals and Their Associated Possibilistic Variables 5 A Boolean Algebraic Structure on the Set of Compound Conditionals 6 Possibility Measures on T(A) and Canonical Extensions 7 Conclusions References Adding Semantics to Fuzzy Similarity Measures Through the d-Choquet Integral 1 Introduction 2 Preliminaries 3 Fuzzy d-Choquet Similarity Measures 4 Similarity Learning 5 Conclusion References Integrating Evolutionary Prejudices in Belief Function Theory 1 Introduction 2 Background 2.1 Basics About Belief Functions 2.2 Defiance, Retraction and Latent Structures 2.3 Conditioning in Dempster and Revision 3 Formalizing Prejudices 3.1 Case 1: Information and Prejudice Focused on the Same Subset A= and A= 3.2 Case 2: Evidence on A and Prejudice Against B with AB 3.3 Case 3: Evidence on A and Prejudice Against B with BA 3.4 Case 4: Evidence on A= and Prejudice Against B= with (AB)= and (BA)= 4 Properties 5 Discussion and Related Work References Special Track on AI and Heterogeneous Data Multi-label Classification of Mobile Application User Reviews Using Neural Language Models 1 Introduction 2 Background 2.1 Multi-Label Classification 2.2 Neural Language Models 3 Methodology 3.1 Data Construction 3.2 Reviews Classification Model 3.3 New Review Classification 4 Experimental Study 4.1 Dataset and Experimental Environment 4.2 Hyper-parameters Configuration 4.3 Results 5 Conclusion References Provenance Calculus and Possibilistic Logic: A Parallel and a Discussion 1 Introduction 2 Provenance Calculus 3 Possibilistic Logic 3.1 Basic Possibilistic Logic and Variants 3.2 Weights in Partially Ordered Structures 3.3 Other Products and Polynomials 3.4 Databases and Possibilistic Logic 4 Provenance and Possibilistic Logic - A Final Discussion References Hypergraphs in Logic Programming 1 Introduction 2 Preliminaries 2.1 Multi-adjoint Normal Logic Programming 2.2 Basic Definitions of Hypergraphs 3 Logic Normal Programs Through Hypergraphs 4 Applications of Hypergraph Representation 5 Conclusions and Future Work References Macsum Aggregation Learning and Missing Values 1 Introduction 2 Preliminaries 2.1 Notations 2.2 Definitions 3 Operator Based Aggregation 3.1 Operators 3.2 Aggregation 3.3 Extending Operator-Based Aggregation to Interval Data 3.4 Learning an Operator Based Aggregation 4 Experiments 4.1 Data-Set 4.2 How Can Missing Values be Accounted For? 4.3 Running the Experiment 4.4 Results 5 Conclusion References GAINS: Comparison of Genetic AlgorIthms and Neural AlgorithmS for Video Game Playing 1 Background and Contribution 2 Results 2.1 Genetic Algorithms and Neural Networks 2.2 Testing Environment: PyBoy and Gym Retro 2.3 Implementing Genetic Algorithms 2.4 Implementing the Hybrid Approach 2.5 Empirical Results and Discussion 3 Concluding Remarks References Author Index