Multicriteria decision aid and artificial intelligence : links, theory and applications

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Cover; Title Page; Copyright; Contents; Preface; Notes on Contributors; Part I The Contributions of Intelligent Techniques in Multicriteria Decision Aiding; Chapter 1 Computational intelligence techniques for multicriteria decision aiding: An overview; 1.1 Introduction; 1.2 The MCDA paradigm; 1.2.1 Modeling process; 1.2.2 Methodological approaches; 1.2.2.1 Multiobjective mathematical programming; 1.2.2.2 Multiattribute utility/value theory; 1.2.2.3 Outranking techniques; 1.2.2.4 Preference disaggregation analysis; 1.3 Computational intelligence in MCDA

1.3.1 Statistical learning and data mining1.3.1.1 Artificial neural networks; 1.3.1.2 Rule-based models; 1.3.1.3 Kernel methods; 1.3.2 Fuzzy modeling; 1.3.2.1 Fuzzy multiobjective optimization; 1.3.2.2 Fuzzy preference modeling; 1.3.3 Metaheuristics; 1.3.3.1 Evolutionary methods and metaheuristics in multiobjective optimization; 1.3.3.2 Preference disaggregation with evolutionary techniques; 1.4 Conclusions; References; Chapter 2 Intelligent decision support systems; 2.1 Introduction; 2.2 Fundamentals of human decision making; 2.3 Decision support systems

2.4 Intelligent decision support systems2.4.1 Artificial neural networks for intelligent decision support; 2.4.2 Fuzzy logic for intelligent decision support; 2.4.3 Expert systems for intelligent decision support; 2.4.4 Evolutionary computing for intelligent decision support; 2.4.5 Intelligent agents for intelligent decision support; 2.5 Evaluating intelligent decision support systems; 2.5.1 Determining evaluation criteria; 2.5.2 Multi-criteria model for IDSS assessment; 2.6 Summary and future trends; Acknowledgment; References

Part II Intelligent Technologies for Decision Support and Preference ModelingChapter 3 Designing distributed multi-criteria decision support systems for complex and uncertain situations; 3.1 Introduction; 3.2 Example applications; 3.3 Key challenges; 3.4 Making trade-offs: Multi-criteria decision analysis; 3.4.1 Multi-attribute decision support; 3.4.2 Making trade-offs under uncertainty; 3.5 Exploring the future: Scenario-based reasoning; 3.6 Making robust decisions: Combining MCDA and SBR; 3.6.1 Decisions under uncertainty: The concept of robustness; 3.6.2 Combining scenarios and MCDA

3.6.3 Collecting, sharing and processing information: A distributed approach3.6.4 Keeping track of future developments: Constructing comparable scenarios; 3.6.5 Respecting constraints and requirements: Scenario management; 3.6.6 Assisting evaluation: Assessing large numbers of scenarios; 3.6.6.1 Comparing single scenarios: Exploring the stability of consequences; 3.6.6.2 Considering multiple scenarios: Aggregation techniques; 3.7 Discussion; 3.8 Conclusion; Acknowledgment; References; Chapter 4 Preference representation with ontologies; 4.1 Introduction; 4.2 Ontology-based preference models

4.3 Maintaining the user profile up to date

Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Mul ...

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