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.

Decision making is a multi-faceted and challenging, yet important task. A decision maker normally has to take into consideration a number of alternatives, which often conflict with one another, before reaching a good decision. To cope with the challenges of decision making, decision support systems have been developed to provide assistance in human decision making processes. The key to decision support systems is to collect information/data, analyse the information/data collected, and subsequently make quality and informed decisions. In this aspect, intelligent reasoning and learning techniques have emerged as a powerful approach to solving real-world decision making problems. The main aim of this research handbook is to present a small fraction of techniques stemmed from artificial intelligence, as well as other complementary methodologies, that are useful for developing intelligent decision support systems. In addition, application examples on how the intelligent decision support systems can be deployed to undertake decision making problems in a variety of domains are presented. Among the topics covered in this book include • modelling and design of intelligent decision support systems • artificial neural networks, genetic algorithm, and fuzzy systems for intelligent decision making • case based reasoning and agent-based systems for intelligent decision making • application of intelligent decision support systems to business, management, manufacturing, engineering, biomedicine, transportation and food industries.