Cognitive Informatics and Computational Intelligence: Theory and Applications

Preface This special issue of Fundamenta Informaticae focuses on the foundations and applications of Cog-nitive Informatics and Computational Intelligence (briefly, CI2). CI2 focuses on studies of human information processing as well as the byproducts of perception and cognition. Cognitive Informatics (CI) is a multidisciplinary study of cognition, computing and information sciences which investigates the information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Specifically, CI2 provides a coherent set of fundamental theories and contemporary mathematics that form the foundation for most science and engineering disciplines such as applied mathematics (e.g. via near and far). This special issue presents some of the latest advances in cognitive informatics and cognitive computing. A total of 11 papers were accepted for publication. Each accepted paper has undergone a thorough review (at least two reviewers for each paper) and a second round of review and revision cycle. Strategies for NP-hard Optimization Problems, present analysis of performance of humans when solving NP-complete problems. These analyses are supported by experiments which include the human capability to compute good suboptimal solutions to these problems, and the authors try to identify the kind of problem instances which make humans compute the best and worst solutions (including the dependance of their performance on the size of problem instances). Finally, their performance with computational heuristics typically used to approximately solve these problems are compared, and participants in these experiments are also interviewed in order to infer the most typical strategies used by them, as well as how these strategies depend on the form and size of problem instances. on tolerance rough sets model(TRSM) to model document-term relations in text mining. Specifically, this representation maps the terms occurring in TRSM-representation to terms in the lexicon, hence the final representation of a document is a weight vector consisting only of terms that occurred in the lexicon (lexicon-representation). The lexicon-representation can be viewed as a compact form of TRSM-v-viii v