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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

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Cover of 'Symbolic and Quantitative Approaches to Reasoning with Uncertainty'

Table of Contents

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    Book Overview
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    Chapter 1 Cautious Decision-Making for Tree Ensembles
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    Chapter 2 Enhancing Control Room Operator Decision Making: An Application of Dynamic Influence Diagrams in Formaldehyde Manufacturing
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    Chapter 3 Decision with Belief Functions and Generalized Independence: Two Impossibility Theorems
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    Chapter 4 A Logical Framework for User-Feedback Dialogues on Hypotheses in Weighted Abduction
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    Chapter 5 Modifications of the Miller Definition of Contrastive (Counterfactual) Explanations
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    Chapter 6 Revisiting Approximate Reasoning Based on Grounded Semantics
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    Chapter 7 Extension-Based Semantics for Incomplete Argumentation Frameworks: Grounded Semantics and Principles
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    Chapter 8 An Equivalence Class of Gradual Semantics
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    Chapter 9 Determining Preferences over Extensions: A Cautious Approach to Preference-Based Argumentation Frameworks
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    Chapter 10 A Ring-Based Distributed Algorithm for Learning High-Dimensional Bayesian Networks
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    Chapter 11 On Identifiability of BN2A Networks
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    Chapter 12 Normative Monitoring Using Bayesian Networks: Defining a Threshold for Conflict Detection
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    Chapter 13 An Optimized Quantum Circuit Representation of Bayesian Networks
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    Chapter 14 A Comparison of Different Marginalization Operations in Simple Propagation
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    Chapter 15 Approximations of System W Between c-Inference, System Z, and Lexicographic Inference
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    Chapter 16 First Steps Towards a Logic of Ordered Pairs
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    Chapter 17 Representing Nonmonotonic Inference Based on c-Representations as an SMT Problem
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    Chapter 18 On the Cognitive Logic of Human Propositional Reasoning: Merging Ranking Functions
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    Chapter 19 Handling Inconsistency in (Numerical) Preferences Using Possibility Theory
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    Chapter 20 Evidential Generative Adversarial Networks for Handling Imbalanced Learning
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    Chapter 21 Learning Sets of Probabilities Through Ensemble Methods
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    Chapter 22 Neural Graphical Models
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    Chapter 23 An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models
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    Chapter 24 PETS: Predicting Efficiently Using Temporal Symmetries in Temporal PGMs
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    Chapter 25 Lifting Factor Graphs with Some Unknown Factors
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    Chapter 26 On the Enumeration of Non-dominated Spanning Trees with Imprecise Weights
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    Chapter 27 A Robust Bayesian Approach for Causal Inference Problems
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    Chapter 28 Conditional Objects as Possibilistic Variables
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    Chapter 29 Adding Semantics to Fuzzy Similarity Measures Through the d-Choquet Integral
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    Chapter 30 Integrating Evolutionary Prejudices in Belief Function Theory
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    Chapter 31 Multi-label Classification of Mobile Application User Reviews Using Neural Language Models
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    Chapter 32 Provenance Calculus and Possibilistic Logic: A Parallel and a Discussion
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    Chapter 33 Hypergraphs in Logic Programming
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    Chapter 34 Macsum Aggregation Learning and Missing Values
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    Chapter 35 GAINS: Comparison of Genetic AlgorIthms and Neural AlgorithmS for Video Game Playing
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Title
Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Published by
Springer Nature Switzerland, November 2023
DOI 10.1007/978-3-031-45608-4
ISBNs
978-3-03-145607-7, 978-3-03-145608-4
Editors

Bouraoui, Zied, Vesic, Srdjan

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