Computational Intelligence: Past, Today, and Future -- Uncertainty in Dynamically Changing Input Data -- Decision Making under Uncertainty by Possibilistic Linear Programming Problems -- Intelligent DecisionMaking in Training Based on Virtual Reality -- A Many-Valued Temporal Logic and Reasoning Framework for Decision Making -- A Statistical Approach to Complex Multi-Criteria Decisions -- A Web Based Assessment Tool via the Evidential Reasoning Approach -- An Intelligent Policy Simulator for Supporting Strategic Nuclear Policy Decision Making -- Computing withWords for Hierarchical and Distributed Decision-Making -- Realizing Policies by Projects Using Fuzzy Multiple Criteria Decision Making -- Evolutionary ComputationMethods for Fuzzy Decision Making on Load Dispatch Problems -- Intelligent Decision-Making for a Smart Home Environment with Multiple Occupants -- Applying a Choquet Integral Based Decision Making Approach to Evaluate Agile Supply Chain Strategies.

In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.