Compare sub - goal with element of input statement
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Try new sentence
Make appropriate substitutions (Including new sub-goal)
Eliminate new statement new statement,
new sub-goar
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Figure 3
Replace with old statement, old sub-goal
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CONTRANS (Conceptual Thought, Random-Net Simulation) / 133 matching and list-making processes used in
Phase I.
It should be noted that the neurodynamic nets, with their many parallel processes, are capable of recognizing stimuli without the many consecutive logical steps taken in sequential digital recognition or matching techniques. Thus, a physical embodiment of the full CONTRANS model would be a step toward combining the perceptive speed of a perceptron-type network with the flexibility and logical power of heuristic programming.
However, since Simulating a parallel operat-ing network on a serially operatoperat-ing computer is a rather awkward process, the advantages of connective nets will not be reflected in actual running time.
The design of neurodynamic networks for CONTRANS presents several special prob-lems. These problems arise from the fact that, while most neural net simulations are concerned with the discrimination of statis-tically describable stimuli such as geometri-cal figures, CONTRANS is concerned with the recognition and manipulation of symbol chains, expressions which are already con-ceptualized.
It will be assumed that geometrically perceiving networks, such as the perceptrons of Rosenblatt, are capable of recognizing letters and of representing different char-acters by the firing of different neurons. It will be assumed that the output of these neu-rons will be the input to the CONTRANS nets selection of an appropriate expression for the growth of the amplification value of a particular interneural pathway as a function of excitation and reinforcement.
For example:
This is a simple but satisfactory equation for the weight of the pathway leading from N i to N j . As simulated by FORTRAN pro-grams for the mM 1620 (with Vo equalling 0.2 and 0.4), Vi j rises sharply toward 1 with the first simulatneous firing of N i and N.
and then tends asymptotically towards 1 with additional simultaneous excitations. With a firing of N i without N j ' Vi j decreases sharply toward zero and tends asymptotically toward zero with further such firings. Thus this growth function allows sharp-almost "yes-no"-learning in one trial. Such a sharply reacting growth function would play havoc with the statistical sophistication of a geo-metrical perceptron. However, this equation would be quite suitable for the one trial as-sociation of two abstractions or identified symbols.
The second requirement, for recognition of order in time, will be met through the provision of reverberatory networks and neurons representing intermediate parts of words, such as syllables, and intermediate parts of sentences, such as phrases.
In the future, it should be possible to make more of a serious attempt at deSigning CON-TRANS random nets that better fit the char-acteristics of the nervous system without changing the logic of the system as a whole.
In Phase III, CONTRANS will be used in the formation and testing of inductive hypo-theses, and will thus attempt to make general-izations from separate occurrences.
-It is hoped that, in Phase IV, constraints on the form of input sentences, particularly in regard to the use of parentheses, will be removed. An attempt will be made to divide
"screen-memory" into temporary or per-manent cumulative segments to reduce un-necessary scanning time.
In addition, there are possibilities for special applications and special embodiments.
In final form, and in special-purpose em-bodiment, the system would accept ordinary sentences as instructions and data, would process them (perhaps in a not easily pre-dictable manner) with the benefit of accumu-lated data and conclusions and, due to its parallel form, would in general, operate with much greater speed then its computer simu-lations.
134 / Computers - Key to Total Systems Control References l. Hebb, D., The Organization of Behavior,
John Wiley and Sons, New York, 1949.
2. Rosenblatt, F., The Perceptron - A Theory of Statistical Separability in Cognitive Systems, Cornell Aeronautical Labora-tory, 1958.
3. Uttley, A., "Conditional Probability Ma-chines and Conditional Reflexes," in Auto-mata Studies, ed. by C. E. Shannon and J. McCarthy; Princeton University Press, 1956.
4. Minsky, M., "Some Methods of Artificial Intelligence and Heuristic Programming,"
in Mechanisation of Thought Processes, Her Majesty's Stationery Office, London, 1959.
5. McCarthy, J., "Programs With Common Sense," in Mechanisation of Thought Proc-esses, Her Majesty's Stationery Office, London, 1959.
6. Simon, H., "Modeling Hum a n Thought Process," Proceedings 0 f the Western Joint Computer Conference, National Joint Computer Committee, 1961.
7. Penfield, W. and Roberts, L., Speech and Brain-Mechanisms, Princeton University Press, Princeton, 1956.
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"Tests on a Cell Assembly Theory of the Action of the Brain, USing a Large Computer," in Transactions of Informa-tion Theory, IRE, 1956.
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