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Preface; References; Contents; Why Deep Neural Networks: A Possible Theoretical Explanation; 1 Formulation of the Problem; 2 Why Deep Neural Networks: Our Explanation; 3 Conclusion; References; Abstract Argumentation Frameworks to Promote Fairness and Rationality in Multi-experts Multi-criteria Decision Making; 1 Introduction; 2 Preliminary Notions; 2.1 Multi-criteria Decision Making (MCDM); 2.2 Argumentation Frameworks; 3 Proposed Model for MEMCDM Using Argumentation Frameworks; 3.1 Arguments; 3.2 Attacks; 4 A Simple Example; 4.1 Towards Decision Making; 5 Conclusion and Future Work.
1 Need for Range Estimation Under Constraints2 Known Results: Brief Reminder; 3 New Result: Discontinuity Is the only Obstacle to Computing underlineY and overlineY; References; Towards a Physically Meaningful Definition of Computable Discontinuous and Multi-valued Functions (Constraints); 1 Formulation of the Problem; 2 Towards a New Definition of Computable Discontinuous and Multi-valued Functions; 3 Properties of the New Definition; References; Algebraic Product is the only T-norm for Which Optimization Under Fuzzy Constraints is Scale-Invariant; 1 Formulation of the Problem.
2 Main ResultsReferences; Comparing Operation Points in Linear Programming with Fuzzy Constraints; 1 Introduction; 2 The Fuzzy Linear Programming Model; 3 Concepts of Optimality Under Fuzzy Uncertainty; 3.1 Fuzzy Global Optimal Solution; 4 Ranking a Crisp Solution; 4.1 Operation Points; 4.2 Application Example; 5 Concluding Remarks; References; On Modeling Multi-experts Multi-criteria Decision-Making Argumentation and Disagreement: Philosophical and Computational Approaches Reconsidered; 1 Introduction; 2 Conceptualizing Disagreement Among Experts as Disagreement Among Epistemic Peers.
3 Expert Disagreement: Epistemic and Pragmatic Rationality3.1 Epistemic Rationality: A More Subtle Focus of Disagreement on Epistemic Justification; 3.2 Pragmatic Rationality; 3.3 Synchronic and Diachronic Rationality, Global and Local; 4 Computational Modeling: Descriptive Constraints for Epistemic and Pragmatic Disagreements; 5 Preliminary Notions About Argumentation Frameworks and MEMCDM; 5.1 Arguments; 5.2 Attacks; 6 How Epistemic and Pragmatic Disagreements Can Help MEMCDM; 7 What's Next?; References.
1 Need for Range Estimation Under Constraints2 Known Results: Brief Reminder; 3 New Result: Discontinuity Is the only Obstacle to Computing underlineY and overlineY; References; Towards a Physically Meaningful Definition of Computable Discontinuous and Multi-valued Functions (Constraints); 1 Formulation of the Problem; 2 Towards a New Definition of Computable Discontinuous and Multi-valued Functions; 3 Properties of the New Definition; References; Algebraic Product is the only T-norm for Which Optimization Under Fuzzy Constraints is Scale-Invariant; 1 Formulation of the Problem.
2 Main ResultsReferences; Comparing Operation Points in Linear Programming with Fuzzy Constraints; 1 Introduction; 2 The Fuzzy Linear Programming Model; 3 Concepts of Optimality Under Fuzzy Uncertainty; 3.1 Fuzzy Global Optimal Solution; 4 Ranking a Crisp Solution; 4.1 Operation Points; 4.2 Application Example; 5 Concluding Remarks; References; On Modeling Multi-experts Multi-criteria Decision-Making Argumentation and Disagreement: Philosophical and Computational Approaches Reconsidered; 1 Introduction; 2 Conceptualizing Disagreement Among Experts as Disagreement Among Epistemic Peers.
3 Expert Disagreement: Epistemic and Pragmatic Rationality3.1 Epistemic Rationality: A More Subtle Focus of Disagreement on Epistemic Justification; 3.2 Pragmatic Rationality; 3.3 Synchronic and Diachronic Rationality, Global and Local; 4 Computational Modeling: Descriptive Constraints for Epistemic and Pragmatic Disagreements; 5 Preliminary Notions About Argumentation Frameworks and MEMCDM; 5.1 Arguments; 5.2 Attacks; 6 How Epistemic and Pragmatic Disagreements Can Help MEMCDM; 7 What's Next?; References.