Intelligent systems architecture: Design techniques

The purpose in modern intelligent systems design is to specify, design and implement systems that have a high degree of machine intelligence. Machine intelligence can be defined as the ability to emulate or duplicate the sensory processing and decision making capabilities of human beings in computing machines (Barr and Feigenbaum, 1981). Intelligent systems need the ability to learn autonomously and to adapt in uncertain or partially-known environments if they are to progress past the academic domain and into a full engineering implementation. Different approaches have been utilized that either take advantage of one particular artificial intelligence methodology or exploit the complementary properties of several techniques to achieve a common goal.

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

[2]  P.J. Antsaklis,et al.  Implementations of learning control systems using neural networks , 1992, IEEE Control Systems.

[3]  Barr and Feigenbaum Edward A. Avron.  The Handbook of Artificial Intelligence , 1981 .

[4]  David E. van den Bout,et al.  A traveling salesman objective function that works , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[6]  Victor R. Lesser,et al.  The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty , 1980, CSUR.

[7]  H. Penny Nii,et al.  Blackboard Systems, Part One: The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures , 1986, AI Mag..

[8]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[9]  T. Kohonen,et al.  Statistical pattern recognition with neural networks: benchmarking studies , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  C. Hewitt.  The challenge of open systems , 1990 .

[11]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[12]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[14]  Abraham Kandel,et al.  Hybrid Architectures for Intelligent Systems , 1992 .

[15]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[16]  Renato De Mori,et al.  A hybrid coder for hidden Markov models using a recurrent neural networks , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[17]  Huan Liu,et al.  Building a generic architecture for robot hand control , 1988, IEEE 1988 International Conference on Neural Networks.

[18]  K.S. Narendra,et al.  Intelligent control using neural networks , 1992, IEEE Control Systems.

[19]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[20]  Kazuyoshi Tsutsumi,et al.  Neural computation for controlling the configuration of 2-dimensional truss structure , 1988, IEEE 1988 International Conference on Neural Networks.

[21]  Roberto Serra,et al.  Complex Systems and Cognitive Processes , 1990, Springer Berlin Heidelberg.

[22]  H. Penny Nii,et al.  Blackboard Systems, Part Two: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective , 1986, AI Mag..

[23]  Mo M. Jamshidi,et al.  Neural networks in robotics: A survey , 1990, J. Intell. Robotic Syst..

[24]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[27]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[28]  H. Penny Nii,et al.  Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architectures , 1995 .