Soft Computing in decision modeling

Soft computing offers a large variety of tools for decision making. This special issue focuses on different aspects related to soft computing and its application to decision problems. Papers were selected among the ones presented at the 5th Interantional Conference on Modeling Decisions for Artificial Intelligence (MDAI 2008) celebrated in Sabadell (Catalonia, Spain). Eight of the papers included in this issue are more oriented to the theoretical foundations of the tools, while the others focus on the applications. With respect to the former, the issue deals with the following topics related to theoretical foundations. Aggregation operators (Torra and Narukawa 2007) are well-recognized tools within decision making. They focus on the problem of fusing different opinions or criteria, and, as such, they permit users and systems to select on the basis of synthesized, and hopefully better, information. Clustering algorithms are tools for unsupervised machine learning, or, similarly, for unsupervised knowledge discovery. They explicitly permit us to make the distinctions between collections of objects, so decisions can be done later under a better understanding of the nature of the data. Rough sets have also been used in decision-making problems when information is vague, ambiguous or not complete. An extension of rough set theory was developed for multi-criteria decision problems. That is, the dominance-based rough set approach (Greco et al. 2001). In the next section, we describe in more detail the structure of the issue introducing the papers. As it will be seen later, eight of the papers focus on the foundations of decision, and the rest of the papers (three) are more application oriented.