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Decision making arises when we wish to select the best possible course of action from a set of alternatives. With advancements of the digital technologies, it is easy, and almost instantaneous, to gather a large volume of information and/or data pertaining to a problem that we want to solve. For instance, the world-wi- web is perhaps the primary source of information and/or data that we often turn to when we face a decision making problem. However, the information and/or data that we obtain from the real world often are complex, and comprise various kinds of noise. Besides, real-world information and/or data often are incomplete and ambiguous, owing to uncertainties of the environments. All these make decision making a challenging task. To cope with the challenges of decision making, - searchers have designed and developed a variety of decision support systems to provide assistance in human decision making processes. The main aim of this book is to provide a small collection of techniques stemmed from artificial intelligence, as well as other complementary methodo- gies, that are useful for the design and development of intelligent decision support systems. Application examples of how these intelligent decision support systems can be utilized to help tackle a variety of real-world problems in different - mains, e. g. business, management, manufacturing, transportation and food ind- tries, and biomedicine, are also presented. A total of twenty chapters, which can be broadly divided into two parts, i. e.
Table des matières
Modelling and Design Techniques for Intelligent Decision Support Systems.- Advances in Intelligent Decision Making.- IDSSE-M: Intelligent Decision Support Systems Engineering Methodology.- Shape Design of Products Based on a Decision Support System.- Enhancing Decision Support System with Neural Fuzzy Model and Simple Model Visualizations.- Computational Agents in Complex Decision Support Systems.- A Multi-criteria Decision-Support Approach to Sustainable Rural Energy in Developing Countries.- A Decision Making System Based on Complementary Learning.- A Forecasting Support System Based on Exponential Smoothing.- Reinforcement Based U-Tree: A Novel Approach for Solving POMDP.- On the Use of Fuzzy Inference Systems for Assessment and Decision Making Problems.- Reviews and Applications of Intelligent Decision Support Systems.- Decision Support Systems in Transportation.- Decision Support Systems for the Food Industry.- Building a Decision Support System for Urban Design Based on the Creative City Concept.- Fuzzy Prices in Combinatorial Auction.- Application of Artificial Neural Network to Fire Safety Engineering.- Decision-Making for the Optimal Strategy of Population Agglomeration in Urban Planning with Path-Converged Design.- A Cognitive Interpretation of Thermographic Images Using Novel Fuzzy Learning Semantic Memories.- Adaptive Fuzzy Inference Neural Network System for EEG Signal Classification.- A Systematic Approach to the Design of a Case-Based Reasoning System for Attention-Deficit Hyperactivity Disorder.- A NeuroCognitive Approach to Decision Making for the Reconstruction of the Metabolic Insulin Profile of a Healthy Person.- Erratum.- Erratum: Fuzzy Prices in Combinatorial Auction.
Résumé
Decision making arises when we wish to select the best possible course of action from a set of alternatives. With advancements of the digital technologies, it is easy, and almost instantaneous, to gather a large volume of information and/or data pertaining to a problem that we want to solve. For instance, the world-wi- web is perhaps the primary source of information and/or data that we often turn to when we face a decision making problem. However, the information and/or data that we obtain from the real world often are complex, and comprise various kinds of noise. Besides, real-world information and/or data often are incomplete and ambiguous, owing to uncertainties of the environments. All these make decision making a challenging task. To cope with the challenges of decision making, - searchers have designed and developed a variety of decision support systems to provide assistance in human decision making processes. The main aim of this book is to provide a small collection of techniques stemmed from artificial intelligence, as well as other complementary methodo- gies, that are useful for the design and development of intelligent decision support systems. Application examples of how these intelligent decision support systems can be utilized to help tackle a variety of real-world problems in different - mains, e. g. business, management, manufacturing, transportation and food ind- tries, and biomedicine, are also presented. A total of twenty chapters, which can be broadly divided into two parts, i. e.
