Modeling Decisions for Artificial Intelligence : 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings / / edited by Vicenc Torra, Torra Narukawa

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• Edizione: 1st ed. 2015.
• Descrizione fisica: 1 online resource (XXVI, 243 p. 39 illus.)
• Disciplina: 006.3015118
• Soggetto topico: Pattern recognition; Data mining; Application software; Information storage and retrieval; Numerical analysis; Artificial intelligence; Pattern Recognition; Data Mining and Knowledge Discovery; Information Systems Applications (incl. Internet); Information Storage and Retrieval; Numeric Computing; Artificial Intelligence
• Persona (resp. second.): TorraVicenc; NarukawaTorra
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di contenuto: Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Modeling the Complex Search Space of DataPrivacy Problems -- Classifying Large Graphswith Differential Privacy -- Statistical Forecasting Using Belief Functions -- Preference Learning: Machine Learning MeetsPreference Modeling -- Game-Theoretic Approaches to DecisionMaking in Cyber-Physical Systems Security(Extended Abstract) -- Contents -- Invited Paper -- Classifying Large Graphs with Differential Privacy -- 1 Introduction -- 2 Background -- 2.1 Graph Kernels -- 2.2 Differential Privacy and Graphs -- 3 Private Kernels for Large Graphs -- 3.1 Private Graphlet Kernels -- 3.2 Private p-random Walk Kernels -- 4 Scaling Private Graphlet Kernels -- 4.1 Differential Privacy with Sampling -- 5 Experiments -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Classification Results -- 5.4 Sampling the Private Graphlet Kernel -- 6 Related Work -- 7 Conclusions -- References -- Aggregation Operators and Decision Making -- Extremal Completions of Triangular Norms Known on a Subregion of the Unit Interval -- 1 Introduction -- 2 Basic Notions and Results -- 3 Extremal Extensions of t-norms Without a Non-trivial Idempotent Element in [a,b] -- 3.1 Case When a=b -- 3.2 Case when a=0 -- 3.3 Case When b=1 -- 3.4 Case When 0 ; 5 Construction of Two-Dimensional Utilities from One-Dimensional Utilities -- References -- A Comparison of the GAI Model and the Choquet Integral w.r.t. a k-ary Capacity -- 1 Introduction -- 2 Basic Definitions -- 2.1 (k-ary) Capacity and the Choquet Integral -- 2.2 GAI Model -- 3 Link Between the GAI Model and a k-ary Capacity When All Attributes Are Discrete -- 4 A Much Cheaper Description of the Monotonicity Conditions When All Attributes Are Discrete -- 4.1 Complexity of the Monotonicity Conditions -- 4.2 Representation Result of 2-additive GAI Models -- 5 Extension of k-ary Choquet Integral -- References -- Estimating Unknown Values in Reciprocal Intuitionistic Preference Relations via Asymmetric Fuzzy Preference Relations -- 1 Introduction -- 2 Preference Relations in Decision Making -- 2.1 Fuzzy Preference Relation -- 2.2 Intuitionistic Fuzzy Preference Relation -- 3 Consistency of Fuzzy Preferences -- 4 Reciprocal Intuitionistic Fuzzy Preference Relations and Asymmetric Fuzzy Preference Relations -- 5 Estimating Unknown Values in Incomplete Reciprocal Intuitionistic Fuzzy Preference Relations -- 6 Conclusion -- References -- Handling Risk Attitudes for Preference Learning and Intelligent Decision Support -- 1 Introduction -- 2 Inferring Preferences from Imprecise Data -- 3 Fuzzy-Linguistic Structures -- 4 Measuring Risk Attitudes with Fuzzy Linguistic Structures -- 5 Learning Types for Decision Support -- 6 Intelligent Decision Support -- 7 Conclusions -- References -- Representation of Ordinal Preferences over Infinite Products -- 1 Introduction -- 2 Notations and Settings -- 2.1 Definitions -- 2.2 Examples -- 2.3 Representation of a Symmetric Preference Structure -- 3 Additive Representation of Independent Symmetric Preference Structure -- 4 Step-Based Preference Structure -- 5 Concluding Remarks -- References -- Clustering and Similarity. ; Spherical k-Means++ Clustering -- 1 Introduction -- 2 Preparation -- 2.1 Spherical k-Means Clustering -- 3 Extension of Dissimilarity in SKM -- 4 Analysis -- 4.1 Preliminary Step -- 4.2 Main Step -- 5 Spherical k-Means++ -- 6 Conclusion -- References -- On Possibilistic Clustering Methods Based on Shannon/Tsallis-Entropy for Spherical Data and Categorical Multivariate Data -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation, Fuzzyc-Means, and Its Variants -- 2.2 Possibilistic Clustering -- 2.3 Fuzzy Clustering for Spherical Data -- 2.4 Fuzzy Clustering for Categorical Multivariate Data -- 3 Proposed Method -- 3.1 Modifying ePCM and tPCM -- 3.2 Possibilistic Clustering for Spherical Data -- 3.3 Possibilistic Clustering for Categorical Multivariate Data -- 4 Numerical Example -- 5 Conclusions -- References -- A Unified Theory of Fuzzy c-Means Clustering Models with Improved Partition -- 1 Introduction -- 2 Background -- 2.1 The Fuzzy c-Means Algorithm -- 2.2 Fuzzy c-Means with Improved Partition -- 2.3 Suppression of Fuzzy c-Means -- 3 The Unified Framework -- 4 Comparative Evaluation -- 5 Conclusions -- References -- Data Mining and Data Privacy -- Effective MVU via Central Prototypes and Kernel Ridge Regression -- 1 Introduction -- 2 Maximum Variance Unfolding -- 3 Selective MVU -- 3.1 Central Prototypes Selection -- 3.2 Complexity of Selective MVU -- 4 Experiments and Results -- 5 Conclusions and Future Extensions -- References -- Cooperative Multi-agent Learning in a Large Dynamic Environment -- 1 Introduction -- 2 Problem Statement -- 3 A Review of the D-DCM-MultiQ Method -- 3.1 Failure to Complete the Hunting Task (Blockage of Learning) -- 3.2 Inappropriate Dynamic Scenario -- 4 Our Proposed Learning Approach for Cooperative Multi-agents Systems in Dynamic Environments -- 4.1 First Proposal: The Action Selection Strategy. ; 4.2 Second Proposal: The Proposed Learning Method Strategy -- 5 Experiments and Analysis -- 5.1 Impact of Least Recently Visited States on the Selection of the Next State -- 5.2 Impact of Increasing the Number of Agents on Learning Performance -- 6 Conclusion -- References -- Optimized and Parallel Query Processing in Similarity-Based Databases -- 1 Introduction -- 2 Generalized Relational Model of Data -- 3 Query Processing -- 3.1 Compilation -- 3.2 Optimizations -- 4 Parallel Query Processing and Experimental Evaluation -- References -- An Evaluation of Edge Modification Techniques for Privacy-Preserving on Graphs -- 1 Introduction -- 1.1 Our Contributions -- 1.2 Notation -- 1.3 Roadmap -- 2 Edge Modification Techniques -- 3 Evaluating Framework -- 3.1 Tested Networks -- 3.2 Information Loss Measures -- 4 Experimental Results -- 5 Conclusions -- References -- Co-utile Collaborative Anonymization of Microdata -- 1 Introduction -- 1.1 Contribution and Plan of this Paper -- 2 Related Work -- 3 Collaborative Anonymization: Requirements and Justification -- 4 Collaborative k-Anonymity -- 5 Collaborative Masking of Confidential Data -- 6 Conclusions and Future Research -- A k-Anonymity -- B Reverse Mapping -- C Co-utility -- References -- Generalization-Based k-Anonymization -- 1 Introduction -- 2 Preliminaries -- 3 An Algorithm for k-Anonymization -- 4 Experiments -- 5 Conclusions -- References -- Logics -- The Complexity of 3-Valued Łukasiewicz Rules -- 1 Introduction -- 2 Preliminaries -- 3 Complexity of the Satisfiability Problem of 3-Valued Łukasiewicz Rules -- 4 Conclusions -- References -- Information Theory for Subjective Logic -- 1 Introduction -- 2 Ellsberg Paradox -- 3 Opinion Representation in Subjective Logic -- 4 Information Theory -- 5 Pignistic Entropy -- 6 Aggregate Uncertainty Entropy -- 7 Ambiguity Entropy -- 7.1 Belief Entropy. ; 7.2 Conceivability Entropy -- 8 Conclusion -- References -- Author Index.
• Sommario/riassunto: This book constitutes the proceedings of the 12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015, held in Skövde, Sweden, in September 2015. The 18 revised full papers presented were carefully reviewed and selected from 38 submissions. They discuss theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques.
• Titolo autorizzato: Modeling Decisions for Artificial Intelligence
• ISBN: 3-319-23240-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483293503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence ; ; 9321