In recent years there has been a growing interest in the need for designing computational intelligence to address complex decision systems. One of the most challenging issues for computational intelligence is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various type of information that is incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. These 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., as 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 computational intelligence. To overview the role of computational intelligence in information–driven complex decision systems and illustrate the potential use and practical applications of computational intelligence related techniques in complex decision systems, this edited volume presents recent research results and provides a state-of-the-art on the future research directions.
|Place of Publication||Paris, France|
|Number of pages||388|
|State||Published - May 2010|
|Name||Atlantis Computational Intelligence Systems|