Started with a paradigm shift in system theory for modeling uncertainty, the past few decades have witnessed an enormous amount of effort on theoretical and practical explorations of intelligent systems that deals with uncertainty and fuzziness inherent in scientific, engineering, and business decision-making processes.
This special issue contains seven articles, which are extended versions of the papers selected from more than 300 presented at the 11th World Congress of International Fuzzy Systems Association (IFSA2005), held in Beijing, People's Republic of China, July 28-31, 2005. Among those candidate papers suggested by initial review and screening, the seven articles have been through a standard process and re-reviewed by at least two referees according to the procedure of the International Journal of Intelligent Systems. The articles in this special issue center on certain important problems of concern and propose approaches to dealing with uncertainty and fuzziness in intelligent systems. Specially, the subjects covered are grouped in four categories, including automation control, knowledge extraction and discovery, information retrieval, and cognitive modeling. The focal points and major contributions of the articles are described as follows:
There are two articles addressing the issues of control systems. Fuzzy logic is used because of its advantage in many cases with simplicity, robustness, and easy optimization. The first article by Mucientes, Alcal'a, Alcal'a-Fdez, and Casillas proposes an approach for learning behaviors in mobile robotics, which consists of a technique to automatically generate input-output data plus a genetic fuzzy search system that obtains cooperative weighted rules. It is considered that rule weights help improve the accuracy of the knowledge base in terms of rule interactiveness, while maintaining a good interpretability. The developed controller has been applied to learn the wall-following behavior, along with tests using a Nomad 200 robot in