An alternative computational model for artificial intelligence

Stuart, Brian Louis. Ph.D., Purdue University, May 1992. An Alternative Computational Model for Artificial Intelligence. Major Professor: Chia-Hong Lee. One objective of artificial intelligence is to mimic the behavior of natural intelligences, and learning is one of the most inherently intelligent activities. Unfortunately, there has not been much success in emulating the low-level learning of classical conditioning and instrumental learning. A novel model of computation, called the cybernetic automaton, which possesses a number of desirable properties, is presented here. First there exists a learning theorem which states that cybernetic automaton learning is in some sense complete. Then through a series of experiments, it is shown that cybernetic automata exhibit many of the properties of classical conditioning and instrumental learning. Finally, the cybernetic automaton model can be implemented using networks of model neurons which are somewhat biologically plausible. Cybernetic automata represent a new and substantial step toward artificially emulating natural behavior.

[1]  Intersensory Interactions in Hermissenda , 1973, The Journal of general physiology.

[2]  Henry Prevost Babbage.  Babbage's calculating engines : a collection of papers , 1982 .

[3]  Daniel L. Alkon,et al.  Responses of Photoreceptors in Hermissenda , 1972, The Journal of general physiology.

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

[5]  D. Alkon.  Changes of Membrane Currents and Calcium-Dependent Phosphorylation during Associative Learning , 1986 .

[6]  D L Alkon,et al.  Sensory convergence on central visual neurons in Hermissenda. , 1980, Journal of neurophysiology.

[7]  Jean,et al.  The Computer and the Brain , 1989, Annals of the History of Computing.

[8]  M. Minsky.  The Society of Mind , 1986 .

[9]  Drew McDermott,et al.  Introduction to artificial intelligence , 1986, Addison-Wesley series in computer science.

[10]  R. J. Nelson,et al.  Introduction to Automata , 1968 .

[11]  D. Alkon.  Responses of hair cells to statocyst rotation , 1975, The Journal of general physiology.

[12]  J. Orbach.  Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[13]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  D. Alkon,et al.  Positive synaptic feedback in visual system of nudibranch mollusk Hermissenda crassicornis. , 1982, Journal of neurophysiology.

[15]  Daniel I. A. Cohen,et al.  Introduction to computer theory , 1986 .

[16]  D. Rumelhart.  Parallel Distributed Processing Volume 1: Foundations , 1987 .

[17]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[18]  T. Crow,et al.  Retention of an associative behavioral change in Hermissenda. , 1978, Science.

[19]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[20]  Azaria Paz,et al.  Some aspects of Probabilistic Automata , 1966, Inf. Control..

[21]  Timur Ash,et al.  Dynamic node creation in backpropagation networks , 1989 .

[22]  R. Kalil.  Synapse formation in the developing brain. , 1989, Scientific American.

[23]  C. Aoki,et al.  Plasticity in brain development. , 1988, Scientific American.

[24]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .