For Recent updates, please visit: http://www.lancs.ac.uk/staff/angelov/EAIS_2013.pdf
The true intelligent systems should be dynamically evolving and be able to adapt and learn. The concept of evolving intelligent systems was established recently as a synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and real time methods for machine learning. This emerging area targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. One of the important research challenges today is to develop methodologies, concepts, algorithms and techniques towards the design of intelligent systems with a higher level of flexibility and autonomy, so that the systems can evolve their structure and knowledge of the environment and ultimately - evolve their intelligence. That is, the system must be able to evolve, to self-develop, to self-organize, to self-evaluate and to self-improve. Wireless sensor networks, assisted ambient intelligence, embedded soft computing diagnostics and prognostics algorithms, intelligent agents, smart evolving sensors; autonomous robotic systems etc. are some of the natural implementation areas of evolving and adaptive intelligent systems. EAIS'13 continues the tradition set by the previous forums (EFS'06, GEFS'08, ESDIS'09, EIS'10) and is supported and organised by the Adaptive and Evolving Fuzzy Systems (AEFS) Task Force, FSTC, CIS, IEEE.
Please forward your proposals with detailed abstract and bio-sketches of the speakers to Symposium Co-Chairs and SSCI Keynote-Tutorial Chair, Dr S Das.
Please forward your special session proposals to Symposium Co-Chairs.
Plamen Angelov, Lancaster
Dimitar Filev, Ford, USA
Nikola Kasabov, Aukland University of Technology, New Zealand
Plamen Angelov (Chair)
Jose Rubio Avila
Dimitar Filev (co-Chair)
Antonio Medina Hernandez
Nik Kasabov (co-Chair)