Recent societal and technological advances allow developing systems composed of large and heterogeneous entities that interact collectively in complex and unpredictable manner. This increase in complexity is characterized by apparently new properties and complex behaviors that emerge as a result of often nonlinear spatiotemporal interactions among a large number of system components at different levels of organization and between system and its environment. 1 Recently, there has been a growing interest for developing mechanisms and methods that allow system entities to collaborate in an intelligent manner. Computational intelligence and agent-based modeling represent novel approaches and promising tools to design and model intelligent systems to solve complex real-world problems. One of the most challenging issues is to handle real-world uncertainties that cannot be eliminated. 2 These uncertainties include various types of information that are incomplete and imprecise. 2 The uncertainties result in a lack of full and precise knowledge of the whole system. 2 Computational intelligent techniques, which are complimentary to the existing traditional techniques, have also shown a great potential to solve these real-world decision problems that evolve in uncertain and dynamic environments.
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Advances in Complex Intelligent Systems
Semantic Scholar · Computer Science · 2016
Abstract
Recent societal and technological advances allow developing systems composed of large and heterogeneous entities that interact collectively in complex and unpredictable manner. This increase in complexity is characterized by apparently new properties and complex behaviors that emerge as a result of often nonlinear spatiotemporal interactions among a large number of system components at different levels of organization and between system and its environment. 1 Recently, there has been a growing interest for developing mechanisms and methods that allow system entities to collaborate in an intelligent manner. Computational intelligence and agent-based modeling represent novel approaches and promising tools to design and model intelligent systems to solve complex real-world problems. One of the most challenging issues is to handle real-world uncertainties that cannot be eliminated. 2 These uncertainties include various types of information that are incomplete and imprecise. 2 The uncertainties result in a lack of full and precise knowledge of the whole system. 2 Computational intelligent techniques, which are complimentary to the existing traditional techniques, have also shown a great potential to solve these real-world decision problems that evolve in uncertain and dynamic environments.