Cognitive Robots

To model the cognitive processes of robot authentication, an emerging technology of cognitive robots is introduced. A number of fundamental problem remains in cognitive robot studies, such as: 1) What are the necessary and sufficient intelligent behaviors of cognitive robots? and 2) How the intelligent capabilities of cognitive robots are distinguished from those of their imperative counterparts? This article presents a cognitive reference model of architectures and behaviors of cognitive robots, which reveals the architectural differences and behavioral characteristics of cognitive robots beyond conventional imperative robots. Cognitive informatics (CI) foundations of cognitive robots are explored from the aspects of neural informatics (Nel) and abstract intelligence (αl). The architectural model of cognitive robots is described based on a layered reference model of the brain (LRMB). The behavioral model of cognitive robot is elaborated with the generic behavioral model and the hierarchical relations among the behavioral processes of cognitive robots. A reference model of cognitive robots (RMCRs) is derived, which models cognitive robots by seven forms of intelligent behaviors at the imperative, autonomic, and cognitive layers from the bottom-up. Applications of RMCR are described in robot authentication, computational intelligence (Col), and automation systems.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Witold Pedrycz,et al.  A Doctrine of Cognitive Informatics (CI) , 2009, Fundam. Informaticae.

[3]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[4]  Shushma Patel,et al.  A layered reference model of the brain (LRMB) , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  A. Grafstein MIT Encyclopedia of the Cognitive Sciences , 2000 .

[6]  James S. Albus,et al.  Outline for a theory of intelligence , 1991, IEEE Trans. Syst. Man Cybern..

[7]  Yingxu Wang,et al.  On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence , 2009, Int. J. Softw. Sci. Comput. Intell..

[8]  R A Brooks,et al.  New Approaches to Robotics , 1991, Science.

[9]  Marina L. Gavrilova,et al.  State-of-the-Art in Robot Authentication [From the Guest Editors] , 2010 .

[10]  Yingxu Wang,et al.  On Contemporary Denotational Mathematics for Computational Intelligence , 2008, Trans. Comput. Sci..

[11]  Yingxu Wang,et al.  Software Engineering Foundations: A Software Science Perspective , 2007 .

[12]  Carla H. Lagorio,et al.  Psychology , 1929, Nature.

[13]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[14]  Alex M. Andrew,et al.  Intelligent Systems: Architecture, Design, and Control , 2002 .

[15]  James S. Albus,et al.  Intelligent Systems: Architectures, Design, Control , 2002 .

[16]  A. Siegman,et al.  Proposal for a , 1959 .

[17]  Yingxu Wang,et al.  On Cognitive Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[18]  W. McCulloch,et al.  Embodiments of Mind , 1966 .

[19]  John Debenham Knowledge systems design , 1989 .

[20]  Randy Goebel,et al.  Computational intelligence - a logical approach , 1998 .

[21]  Gary Riley,et al.  Expert Systems: Principles and Programming , 2004 .

[22]  Yingxu Wang Cognitive Informatics: A New Transdisciplinary Research Field , 2003 .

[23]  Carl Hewitt,et al.  DAI betwixt and between: from 'intelligent agents' to open systems science , 1991, IEEE Trans. Syst. Man Cybern..