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Symbolic and quantitative approaches to reasoning and uncertainty : European conference, ECSQARU'99, London, UK, July 5-9, 1999, proceedings / / Anthony Hunter, Simon Parsons, editors
Symbolic and quantitative approaches to reasoning and uncertainty : European conference, ECSQARU'99, London, UK, July 5-9, 1999, proceedings / / Anthony Hunter, Simon Parsons, editors
Edizione [1st ed. 1999.]
Pubbl/distr/stampa Berlin : , : Springer, , [1999]
Descrizione fisica 1 online resource (IX, 402 p.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Reasoning
Uncertainty (Information theory)
ISBN 3-540-48747-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto On the Dynamics of Default Reasoning -- Nonmonotonic and Paraconsistent Reasoning: From Basic Entailments to Plausible Relations -- A Comparison of Systematic and Local Search Algorithms for Regular CNF Formulas -- Query-answering in Prioritized Default Logic -- Updating Directed Belief Networks -- Inferring Causal Explanations -- A Critique of Inductive Causation -- Connecting Lexicographic with Maximum Entropy Entailment -- Avoiding Non-Ground Variables -- Anchoring Symbols to Vision Data by Fuzzy Logic -- Filtering vs Revision and Update: let us Debate! -- Irrelevance and Independence Axioms in Quasi-Bayesian Theory -- Assessing the value of a candidate -- Learning Default Theories -- Knowledge Representation for Inductive Learning -- Handling Inconsistency Efficiently in the Incremental Construction of Stratified Belief Bases -- Rough Knowledge Discovery and Applications -- Gradient Descent Training of Bayesian Networks -- Open Default Theories over Closed Domains -- Shopbot Economics -- Optimized Algorithm for Learning Bayesian Network from Data -- Merging with Integrity Constraints -- Boolean-like Interpretation of Sugeno Integral -- An Alternative to Outward Propagation for Dempster-Shafer Belief Functions -- On bottom-up pre-processing techniques for automated default reasoning -- Probabilisitc Logic Programming under Maximum Entropy -- Lazy Propagation and Independence of Causal Influence -- A Monte Carlo Algorithm for Combining Dempster-Shafer Belief Based on Approximate Pre-Computation -- An Extension of a lInguistic Negation Model allowing us to Deny Nuanced Property Combinations -- Argumentation and Qualitative Decision Making -- Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach -- State Recognition in Discrete Dynamical Systems using Petri Nets and Evidence Theory -- Robot Navigation and Map Building with the Event Calculus -- Information Fusion in the Context of Stock Index Prediction -- Defeasible Goals -- Logical Deduction using the Local Computation Framework.
Record Nr. UNISA-996465732903316
Berlin : , : Springer, , [1999]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Symbolic and quantitative approaches to reasoning with uncertainty : 16th European conference, ECSQARU 2021, Prague, Czech Republic, September 21-24, 2021 : proceedings / / Jirrina Vejnarova, Nic Wilson (editors)
Symbolic and quantitative approaches to reasoning with uncertainty : 16th European conference, ECSQARU 2021, Prague, Czech Republic, September 21-24, 2021 : proceedings / / Jirrina Vejnarova, Nic Wilson (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (695 pages)
Disciplina 003.54
Collana Lecture notes in computer science
Soggetto topico Uncertainty (Information theory)
ISBN 3-030-86772-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- 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.
Record Nr. UNISA-996464431503316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Symbolic and quantitative approaches to reasoning with uncertainty : 16th European conference, ECSQARU 2021, Prague, Czech Republic, September 21-24, 2021 : proceedings / / Jirrina Vejnarova, Nic Wilson (editors)
Symbolic and quantitative approaches to reasoning with uncertainty : 16th European conference, ECSQARU 2021, Prague, Czech Republic, September 21-24, 2021 : proceedings / / Jirrina Vejnarova, Nic Wilson (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (695 pages)
Disciplina 003.54
Collana Lecture notes in computer science
Soggetto topico Uncertainty (Information theory)
ISBN 3-030-86772-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- 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.
Record Nr. UNINA-9910502665103321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
UAI '04 : proceedings of the 20th conference on Uncertainty in artificial intelligence / / Christopher Meek
UAI '04 : proceedings of the 20th conference on Uncertainty in artificial intelligence / / Christopher Meek
Autore Meek Christopher
Pubbl/distr/stampa Arlington, Virginia : , : AUAI Press, , 2004
Descrizione fisica 1 online resource (xii, 645 pages) : illustrations
Disciplina 006.3
Collana ACM international conference proceeding series
Soggetto topico Artificial intelligence
Uncertainty (Information theory)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910412074403321
Meek Christopher  
Arlington, Virginia : , : AUAI Press, , 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertain graph and network optimization / / Bo Zhang and Jin Peng
Uncertain graph and network optimization / / Bo Zhang and Jin Peng
Autore Zhang Bo
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2022]
Descrizione fisica 1 online resource (144 pages)
Disciplina 519.3
Collana Springer uncertainty research
Soggetto topico Mathematical optimization
Uncertainty (Information theory)
ISBN 981-19-1472-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction Graph and Network Uncertainty Theory Uncertain Programming Uncertain Graph Uncertain Network Optimization Applications of Uncertain Network Optimization
Record Nr. UNINA-9910568280803321
Zhang Bo  
Singapore : , : Springer Nature Singapore Pte Ltd., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Autore Kyburg Henry Ely <1928->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2001
Descrizione fisica 1 online resource (xii, 298 pages) : digital, PDF file(s)
Disciplina 003/.54
Soggetto topico Uncertainty (Information theory)
Probabilities
Logic, Symbolic and mathematical
ISBN 1-107-12249-X
1-280-43029-X
9786610430291
0-511-17479-9
0-511-02068-6
0-511-15484-4
0-511-30239-8
0-511-61294-X
0-511-04748-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Half-title; Title; Copyright; Contents; Preface; 1 Historical Background; 2 First Order Logic; 3 The Probability Calculus; 4 Interpretations of Probability; 5 Nonstandard Measures of Support; 6 Nonmonotonic Reasoning; 7 Theory Replacement; 8 Statistical Inference; 9 Evidential Probability; 10 Semantics; 11 Applications; 12 Scientific Inference; Names Index; Index
Record Nr. UNINA-9910450548703321
Kyburg Henry Ely <1928->  
Cambridge : , : Cambridge University Press, , 2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Autore Kyburg Henry Ely <1928->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2001
Descrizione fisica 1 online resource (xii, 298 pages) : digital, PDF file(s)
Disciplina 003/.54
Soggetto topico Uncertainty (Information theory)
Probabilities
Logic, Symbolic and mathematical
ISBN 1-107-12249-X
1-280-43029-X
9786610430291
0-511-17479-9
0-511-02068-6
0-511-15484-4
0-511-30239-8
0-511-61294-X
0-511-04748-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Half-title; Title; Copyright; Contents; Preface; 1 Historical Background; 2 First Order Logic; 3 The Probability Calculus; 4 Interpretations of Probability; 5 Nonstandard Measures of Support; 6 Nonmonotonic Reasoning; 7 Theory Replacement; 8 Statistical Inference; 9 Evidential Probability; 10 Semantics; 11 Applications; 12 Scientific Inference; Names Index; Index
Record Nr. UNINA-9910782905803321
Kyburg Henry Ely <1928->  
Cambridge : , : Cambridge University Press, , 2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Uncertain inference / / Henry E. Kyburg, Jr. and Choh Man Teng [[electronic resource]]
Autore Kyburg Henry Ely <1928->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2001
Descrizione fisica 1 online resource (xii, 298 pages) : digital, PDF file(s)
Disciplina 003/.54
Soggetto topico Uncertainty (Information theory)
Probabilities
Logic, Symbolic and mathematical
ISBN 1-107-12249-X
1-280-43029-X
9786610430291
0-511-17479-9
0-511-02068-6
0-511-15484-4
0-511-30239-8
0-511-61294-X
0-511-04748-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Half-title; Title; Copyright; Contents; Preface; 1 Historical Background; 2 First Order Logic; 3 The Probability Calculus; 4 Interpretations of Probability; 5 Nonstandard Measures of Support; 6 Nonmonotonic Reasoning; 7 Theory Replacement; 8 Statistical Inference; 9 Evidential Probability; 10 Semantics; 11 Applications; 12 Scientific Inference; Names Index; Index
Record Nr. UNINA-9910815803403321
Kyburg Henry Ely <1928->  
Cambridge : , : Cambridge University Press, , 2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertainty Analyses in Environmental Sciences and Hydrogeology : Methods and Applications to Subsurface Contamination / / Rachid Ababou [and four others]
Uncertainty Analyses in Environmental Sciences and Hydrogeology : Methods and Applications to Subsurface Contamination / / Rachid Ababou [and four others]
Autore Ababou R (Rachid)
Edizione [First edition.]
Pubbl/distr/stampa Singapore : , : Springer, , [2023]
Descrizione fisica 1 online resource (IX, 95 p. 31 illus., 27 illus. in color.)
Disciplina 363.70072
Collana SpringerBriefs in Applied Sciences and Technology Series
Soggetto topico Environmental sciences - Statistical methods
Uncertainty (Information theory)
ISBN 981-9962-41-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction, objectives -- Overview of uncertainty propagation methods -- Review of Probabilistic versus Fuzzy Approaches to Uncertainty Propagation in Geosciences -- Fuzzy set characterization of uncertainty (fuzzy variables) -- Applications of uncertainty analyses on simplified models -- Applications of uncertainty analysis to 3D subsurface contamination problems -- Discussion and conclusions.
Record Nr. UNINA-9910799232603321
Ababou R (Rachid)  
Singapore : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Uncertainty and information : foundations of generalized information theory / / George J. Klir
Uncertainty and information : foundations of generalized information theory / / George J. Klir
Autore Klir George J. <1932->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-Interscience, , c2006
Descrizione fisica 1 online resource (519 p.)
Disciplina 003.54
003/.54
Soggetto topico Uncertainty (Information theory)
Fuzzy systems
ISBN 1-280-24298-1
9786610242986
0-470-31574-1
0-471-75557-5
0-471-75556-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- Acknowledgments -- 1 Introduction -- 1.1. Uncertainty and Its Significance -- 1.2. Uncertainty-Based Information -- 1.3. Generalized Information Theory -- 1.4. Relevant Terminology and Notation -- 1.5. An Outline of the Book -- Notes -- Exercises -- 2 Classical Possibility-Based Uncertainty Theory -- 2.1. Possibility and Necessity Functions -- 2.2. Hartley Measure of Uncertainty for Finite Sets -- 2.2.1. Simple Derivation of the Hartley Measure -- 2.2.2. Uniqueness of the Hartley Measure -- 2.2.3. Basic Properties of the Hartley Measure -- 2.2.4. Examples -- 2.3. Hartley-Like Measure of Uncertainty for Infinite Sets -- 2.3.1. Definition -- 2.3.2. Required Properties -- 2.3.3. Examples -- Notes -- Exercises -- 3 Classical Probability-Based Uncertainty Theory -- 3.1. Probability Functions -- 3.1.1. Functions on Finite Sets -- 3.1.2. Functions on Infinite Sets -- 3.1.3. Bayes' Theorem -- 3.2. Shannon Measure of Uncertainty for Finite Sets -- 3.2.1. Simple Derivation of the Shannon Entropy -- 3.2.2. Uniqueness of the Shannon Entropy -- 3.2.3. Basic Properties of the Shannon Entropy -- 3.2.4. Examples -- 3.3. Shannon-Like Measure of Uncertainty for Infinite Sets -- Notes -- Exercises -- 4 Generalized Measures and Imprecise Probabilities -- 4.1. Monotone Measures -- 4.2. Choquet Capacities -- 4.2.1. Mbius Representation -- 4.3. Imprecise Probabilities: General Principles -- 4.3.1. Lower and Upper Probabilities -- 4.3.2. Alternating Choquet Capacities -- 4.3.3. Interaction Representation -- 4.3.4. Mbius Representation -- 4.3.5. Joint and Marginal Imprecise Probabilities -- 4.3.6. Conditional Imprecise Probabilities -- 4.3.7. Noninteraction of Imprecise Probabilities -- 4.4. Arguments for Imprecise Probabilities -- 4.5. Choquet Integral -- 4.6. Unifying Features of Imprecise Probabilities -- Notes -- Exercises -- 5 Special Theories of Imprecise Probabilities -- 5.1. An Overview -- 5.2. Graded Possibilities -- 5.2.1. Mbius Representation -- 5.2.2. Ordering of Possibility Profiles.
5.2.3. Joint and Marginal Possibilities -- 5.2.4. Conditional Possibilities -- 5.2.5. Possibilities on Infinite Sets -- 5.2.6. Some Interpretations of Graded Possibilities -- 5.3. Sugeno l-Measures -- 5.3.1. Mbius Representation -- 5.4. Belief and Plausibility Measures -- 5.4.1. Joint and Marginal Bodies of Evidence -- 5.4.2. Rules of Combination -- 5.4.3. Special Classes of Bodies of Evidence -- 5.5. Reachable Interval-Valued Probability Distributions -- 5.5.1. Joint and Marginal Interval-Valued Probability Distributions -- 5.6. Other Types of Monotone Measures -- Notes -- Exercises -- 6 Measures of Uncertainty and Information -- 6.1. General Discussion -- 6.2. Generalized Hartley Measure for Graded Possibilities -- 6.2.1. Joint and Marginal U-Uncertainties -- 6.2.2. Conditional U-Uncertainty -- 6.2.3. Axiomatic Requirements for the U-Uncertainty -- 6.2.4. U-Uncertainty for Infinite Sets -- 6.3. Generalized Hartley Measure in Dempster-Shafer Theory -- 6.3.1. Joint and Marginal Generalized Hartley Measures -- 6.3.2. Monotonicity of the Generalized Hartley Measure -- 6.3.3. Conditional Generalized Hartley Measures -- 6.4. Generalized Hartley Measure for Convex Sets of Probability Distributions -- 6.5. Generalized Shannon Measure in Dempster-Shafer Theory -- 6.6. Aggregate Uncertainty in Dempster-Shafer Theory -- 6.6.1. General Algorithm for Computing the Aggregate Uncertainty -- 6.6.2. Computing the Aggregated Uncertainty in Possibility Theory -- 6.7. Aggregate Uncertainty for Convex Sets of Probability Distributions -- 6.8. Disaggregated Total Uncertainty -- 6.9. Generalized Shannon Entropy -- 6.10. Alternative View of Disaggregated Total Uncertainty -- 6.11. Unifying Features of Uncertainty Measures -- Notes -- Exercises -- 7 Fuzzy Set Theory -- 7.1. An Overview -- 7.2. Basic Concepts of Standard Fuzzy Sets -- 7.3. Operations on Standard Fuzzy Sets -- 7.3.1. Complementation Operations -- 7.3.2. Intersection and Union Operations -- 7.3.3. Combinations of Basic Operations.
7.3.4. Other Operations -- 7.4. Fuzzy Numbers and Intervals -- 7.4.1. Standard Fuzzy Arithmetic -- 7.4.2. Constrained Fuzzy Arithmetic -- 7.5. Fuzzy Relations -- 7.5.1. Projections and Cylindric Extensions -- 7.5.2. Compositions, Joins, and Inverses -- 7.6. Fuzzy Logic -- 7.6.1. Fuzzy Propositions -- 7.6.2. Approximate Reasoning -- 7.7. Fuzzy Systems -- 7.7.1. Granulation -- 7.7.2. Types of Fuzzy Systems -- 7.7.3. Defuzzification -- 7.8. Nonstandard Fuzzy Sets -- 7.9. Constructing Fuzzy Sets and Operations -- Notes -- Exercises -- 8 Fuzzification of Uncertainty Theories -- 8.1. Aspects of Fuzzification -- 8.2. Measures of Fuzziness -- 8.3. Fuzzy-Set Interpretation of Possibility Theory -- 8.4. Probabilities of Fuzzy Events -- 8.5. Fuzzification of Reachable Interval-Valued Probability Distributions -- 8.6. Other Fuzzification Efforts -- Notes -- Exercises -- 9 Methodological Issues -- 9.1. An Overview -- 9.2. Principle of Minimum Uncertainty -- 9.2.1. Simplification Problems -- 9.2.2. Conflict-Resolution Problems -- 9.3. Principle of Maximum Uncertainty -- 9.3.1. Principle of Maximum Entropy -- 9.3.2. Principle of Maximum Nonspecificity -- 9.3.3. Principle of Maximum Uncertainty in GIT -- 9.4. Principle of Requisite Generalization -- 9.5. Principle of Uncertainty Invariance -- 9.5.1. Computationally Simple Approximations -- 9.5.2. Probability-Possibility Transformations -- 9.5.3. Approximations of Belief Functions by Necessity Functions -- 9.5.4. Transformations Between l-Measures and Possibility Measures -- 9.5.5. Approximations of Graded Possibilities by Crisp Possibilities -- Notes -- Exercises -- 10 Conclusions -- 10.1. Summary and Assessment of Results in Generalized Information Theory -- 10.2. Main Issues of Current Interest -- 10.3. Long-Term Research Areas -- 10.4. Significance of GIT -- Notes -- Appendix A Uniqueness of the U-Uncertainty -- Appendix B Uniqueness of Generalized Hartley Measure in the Dempster-Shafer Theory -- Appendix C Correctness of Algorithm 6.1.
Appendix D Proper Range of GeneralizedShannon Entropy -- Appendix E Maximum of GSa in Section 6.9 -- Appendix F Glossary of Key Concepts -- Appendix G Glossary of Symbols -- Bibliography -- Subject Index -- Name Index.
Record Nr. UNINA-9910143425103321
Klir George J. <1932->  
Hoboken, New Jersey : , : Wiley-Interscience, , c2006
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