This keynote lecture explores the approaches to implement intelligent behaviors by biological organisms, silicon automata, and computing systems. Autonomous computing is introduced as the latest and advanced computing techniques built upon routine, algorithmic, and adaptive systems. The theory and philosophy behind autonomous computing is cognitive informatics. In other words, autonomous computing systems are applications of cognitive informatics. A Layered Reference Model of the Brain (LRMB) and its cognitive mechanisms and processes are described in this talk, which form the foundation for designing and implementing autonomous computing systems. Real-Time Process Algebra (RTPA) is introduced to formally and rigorously describe autonomous computing systems and cognitive behaviors. It is believed that applications of cognitive informatics and autonomous computing will result in the development of new generation computing architectures and novel information processing systems.

The study of concept formation and learning is a central topic in cognitive informatics. Formal concept analysis can be viewed as an approach on this topic based on a formal context. In this paper, we study some basic data units for formal concept analysis based on hierarchical class analysis. In a formal context, objects and attributes can be grouped into equivalence classes based on equivalence relations. The order relations define the hierarchical structures of the equivalence classes. There exist some basic sets of objects and attributes that are used to not only reflect the equivalence relation and order relation, but also constitute all formal concepts derived from the formal context. The properties of these basic sets are studied and analyzed. 1