Modeling Decisions for Artificial Intelligence -

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Résumé :
This book constitutes the proceedings of the 10th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2013, held in Barcelona, Spain, in November 2013. The 24 papers presented in this volume were carefully reviewed and selected from 40 submissions. They deal with the theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques.

Sommaire:
Theory and Applications of Non-additive Measures and Corresponding Integrals.- Some New Domain Restrictions in Social Choice, and Their Consequences.- Weighted Quasi-Arithmetic Means: Utility Functions and Weighting Functions.- Toward a General Framework for Information Fusion.- Facility Location and Social Choice via Microaggregation.- Ordering Pareto Sets with Fuzzy Inference Systems.- A Comparison of Two Approaches for Situation Detection in an Air-to-Air Combat Scenario.- Web 2.0 Tools to Support Decision Making in Enterprise Contexts.- Using the Logarithmic Generator Function in the Spoken Term Detection Task.- Emotion Detection Using Hybrid Structural and Appearance Descriptors.- A Lazy Learning Approach for Self-training.- Combining Recommender and Reputation Systems to Produce Better Online Advice.- Pushing Constraints into a Pattern-Tree.- Generalization of Quadratic Regularized and Standard Fuzzy c-Means Clustering with Respect to Regularization of Hard c-Means.- Semi-supervised Sequential Kernel Regression Models with Pairwise Constraints.- Query Optimization Strategies in Similarity-Based Databases.- Variables for Controlling Cluster Sizes on Fuzzy c-means.- On Sequential Cluster Extraction Based on L1-Regularized Possibilistic Non-metric Model.- Fast Implementations of Markov Clustering for Protein Sequence Grouping.- The Property of ?2 01-Concordance for Bayesian Confirmation Measures.- Permutability of Fuzzy Consequence Operators Induced by Fuzzy Relations.- Fuzzy Multisets in Granular Hierarchical Structures Generated from Free Monoids.- Landmark Selection for Isometric Feature Mapping Based on Mixed-Integer Optimization.- Rough c-Regression Based on Optimization of Objective Function.- Improving Automatic Edge Selection for Relational Classification.- Analyzing the Impact of Edge Modifications on Networks.