Information technology is playing an increasingly important role in decisionmaking processes, as it infuses pervasively in scientific, engineering, and business domains. Two characteristics of modern information processing have emerged in recent decades, which are themselves interleaving: One is its intelligence and the other is its "softness" (i.e., the capability to deal with uncertainty). This special issue is devoted to intelligent and soft computing techniques for information processing, which represent, manipulate, and extract information that is useful for human reasoning and decisions.
Having undergone a normal peer review procedure, seven articles are included in this special issue. These articles present the extended versions of work selected from the International Conference on Fuzzy Information Processing (FIP2003) held on March 1-4, 2003, in Beijing, China. FIP2003 accepted over 140 refereed papers out of nearly 280 submissions, and featured plenary talks by distinguished scholars such as Lotfi Zadeh, Janusz Kacprzyk, Zeungnam Bien, Laszlo Koczy, Etienne Kerre, and others. The seven articles of this special issue concentrate on certain important problems of concern and propose approaches to intelligent information processing in which uncertainty pertains. Specifically, the subjects covered are grouped in four categories, including information inference, decision science, knowledge discovery, and pattern recognition.
There are two articles addressing the issues of information inference. Primary attention is paid to approximate reasoning. In the first article, Novak and Perfilieva present an approach to perception-based fuzzy logic deduction. In terms of fuzzy IF-THEN rules with linguistic expressions, the work deals with the problem resulting from the nonsmoothness of the piecewise continuous and monotonous function by introducing a fuzzy approximation method named F-transform.