Autonomous Learning Systems: From Data Streams to Knowledge in Real-time

Preface

This book comes as a result of focused research and studies for over a decade in the emerging area that is on the crossroads of a number of well-known and well-established disciplines, such as (Figure 1):

Figure 1 Autonomous learning systems theory is build upon other well-established areas of research (the list is, of course, not exhaustive)

• machine learning (ML);
• system engineering (specifically, system identification), SI;
• data mining, DM;
• statistical analysis, SA;
• pattern recognition including clustering, classification, PR;
• fuzzy logic and fuzzy systems, including neurofuzzy systems, FL;
• and so on.

On the one hand, there is a very strong trend of innovation of all of the above well-established branches of research that is linked to their online and real-time application; their adaptability, flexibility and so on (Liu and Meng, 2004; Pang, Ozawa and Kasabov, 2005; Leng, McGuinty and Prasad, 2005). On the other hand, a very strong driver for the emergence of autonomous learning systems (ALS) is industry, especially defence and security, but also aerospace and advanced process industries, the Internet, eHealth (assisted living), intelligent transport, and so on. The demand in defence was underpinned recently by a range of multimillion research and development projects funded by DARPA, USA (notably, two Grand Challenge competitions (Buehler, Iagnemma and Singh, 2010)); ...