Future Directions for Intelligent Systems and Information Sciences: The Future of Speech and Image Technologies, Brain Computers, WWW, and BioinformaticsNikola Kasabov This edited volume comprises invited chapters that cover five areas of the current and the future development of intelligent systems and information sciences. Half of the chapters were presented as invited talks at the Workshop "Future Directions for Intelligent Systems and Information Sciences" held in Dunedin, New Zealand, 22-23 November 1999 after the International Conference on Neuro-Information Processing (lCONIPI ANZIISI ANNES '99) held in Perth, Australia. In order to make this volume useful for researchers and academics in the broad area of information sciences I invited prominent researchers to submit materials and present their view about future paradigms, future trends and directions. Part I contains chapters on adaptive, evolving, learning systems. These are systems that learn in a life-long, on-line mode and in a changing environment. The first chapter, written by the editor, presents briefly the paradigm of Evolving Connectionist Systems (ECOS) and some of their applications. The chapter by Sung-Bae Cho presents the paradigms of artificial life and evolutionary programming in the context of several applications (mobile robots, adaptive agents of the WWW). The following three chapters written by R.Duro, J.Santos and J.A.Becerra (chapter 3), GCoghill . (chapter 4), Y.Maeda (chapter 5) introduce new techniques for building adaptive, learning robots. |
Contents
ECOS Evolving Connectionist Systems a newold paradigm | 3 |
Evolving ANN controllers for smart mobile robots | 34 |
A simulation environment for the manipulation of naturally | 65 |
Y Maeda | 100 |
Intelligent human computer interaction and scientific | 127 |
Multimodal interactions with agents in virtual worlds | 148 |
A Nijhollt and J Hulstijn | 174 |
New connectionist computational paradigms Brain | 189 |
Suprathreshold stochastic resonance in a neuronal network | 236 |
Information science and bioinformatics | 251 |
Neural network system for promoter recognition | 288 |
Knowledge representation knowledge processing | 307 |
A new paradigm shift from computation on numbers | 329 |
J Kacprzyk | 344 |
Intelligent resource management through the constrained | 373 |
Evaluative studies of fuzzy knowledge discovery through | 387 |
Other editions - View all
Future Directions for Intelligent Systems and Information Sciences: The ... Nikola Kasabov No preview available - 2012 |
Future Directions for Intelligent Systems and Information Sciences: The ... Nikola Kasabov No preview available - 2010 |
Common terms and phrases
acids active adaptive agents algorithm allows analysis applications approach Artificial associated behavior Biology brain called cells cluster complex computing connections considered database defined described determine direction domain dynamic Engineering environment evolution evolutionary evolving example experiments expressed Figure function fuzzy genes genetic given granules human increase input intelligent interaction interest internal knowledge language learning linguistic mean measure mechanisms memory methods modules natural neural networks neurons nodes noise objects obtained operation output particular patterns performance possible prediction presented problem promoter protein quantum quantum mechanics query recognition regions represent representation robot rules Science selection sequence shown shows signal similar simulation solution space specific speech structure Table task techniques threshold types units University variables virtual