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Systems Science and Cybernetics, IEEE Transactions on

Issue 1 • Date March 1968

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Displaying Results 1 - 16 of 16
  • [Table of contents]

    Publication Year: 1968 , Page(s): c1
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  • IEEE Systems Science and Cybernetics Group

    Publication Year: 1968 , Page(s): c2
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  • Change of Editor

    Publication Year: 1968 , Page(s): 1
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  • A Finite-Memory Adaptive Pattern Recognizer

    Publication Year: 1968 , Page(s): 2 - 11
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    This paper gives an adaptive procedure for selecting a discriminant for a pattern recognizer. The optimum discriminant is selected from a given finite set of discriminants. The selection of this set itself is not considered here. At any stage the optimum discriminant is selected on the basis of the past observations. Since the storage space for these observations is assumed to be limited, and hence the qualifier finite memory, the information stored about these past observations is judiciously selected. No other a priori knowledge is assumed. A mathematical model of the problem of pattern recognition is constructed and several theorems are proved. With the help of these theorems, the adaptive procedure is developed. This adaptive procedure is, in effect, a method of using the finite memory efficiently in "training" the pattern recognizer. View full abstract»

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  • Learning Games through Pattern Recognition

    Publication Year: 1968 , Page(s): 12 - 16
    Cited by:  Papers (3)
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    The objective of this research was to investigate a technique for machine learning that would be useful in solving problems involving forcing states. In games or control problems a forcing state is one from which the final goal can always be reached, regardless of what disturbances may arise. A program that learns forcing states in a class of games (in a game-independent format) by working backwards from a previous loss has been written. The class of positions that ultimately results in the opponent's win is learned by the program (using a specially designed description language) and stored in its memory together with the correct move to be made when this pattern reoccurs. These patterns are searched for during future plays of the game. If they are formed by the opponent, the learning program blocks them before the opponent's win sequence can begin. If it forms the patterns first, the learning program initiates the win sequence. The class of games for which the program is effective includes Qubic, Go-Moku, Hex, and the Shannon network games, including Bridge-it. The description language enables the learning program to generalize from one example of a forcing state to all other configurations that are strategically equivalent. View full abstract»

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  • A Feature-Detection Program for Patterns with Overlapping Cells

    Publication Year: 1968 , Page(s): 16 - 23
    Cited by:  Papers (3)
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    An attempt is made to extract feature informations automatically from patterns which may consist of open lines, partially overlapping cells, and cells that may lie entirely inside another cell. The usual pattern-recognition techniques, such as the linear threshold logic technique and the masking or template technique, are not practical here, if not entirely impossible. In this paper, a direct-search computer program using a heuristic approach is described. A test pattern is used to illustrate the capability of the program. The subject should be of general interest to those in the field of automation and cybernetics. View full abstract»

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  • Adaptive Bayes Classification Model with Incompletely Specified Experiment

    Publication Year: 1968 , Page(s): 24 - 28
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    A 2-stage classification model is presented in which the first stage is a quick, computerized Bayes rule decision device, and the second a slow, but perfectly accurate, classifier. A stationary stream of elements or objects to be classified into one of several mutually exclusive categories is fed into the model. The conditional probabilities associated with the Bayes device are assumed unknown at the outset, except up to an initial probability distribution. The a posteriori probabilities from the first stage are treated as information that can speed up or slow down the processing time in the second stage. The latter, after a delay time, feeds back accurate classification information to the first stage to update the conditional probabilities. It is shown that, as the classification process unfolds, any updating scheme that causes the Bayes classifier ultimately to learn the true values of the conditional probabilities also minimizes the expected processing time in the second stage. The learning rate of the system is discussed as a function of the updating scheme. An example of a simple system is presented and the learning rate is derived specifically for that case. View full abstract»

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  • On the Applicability of Wiener's Canonical Expansions

    Publication Year: 1968 , Page(s): 29 - 38
    Cited by:  Papers (3)
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    Previous papers have proposed the application of Wiener's Hermite├é┬┐Laguerre expansion procedure to the multiple-alternative, discrete-decision problem with learning, characteristic of many waveform or stochastic-process pattern-recognition problems. Both sequential and nonsequential procedures were formulated; the resulting models are functionally analogous to a generalized Bayes'-net type of pattern recognizer or decision maker for stochastic processes, differing from usual Bayes' nets in their actual mathematical or circuit configurations and size-determining factors. It is to be noted that for ergodic processes (or approximations thereto), the procedure, if it can be applied, is nonparametric, i.e., not dependent upon prior or explicit knowledge of the form of the probability distribution governing the behavior of the stochastic process. The applicability of the resulting system to problems in cybernetics, intelligence, and learning was discussed previously. The present paper summarizes the results of a subsequent analytical investigation which included a digital simulation of the procedure. Emphasis is on the aspects of realizability, convergence, and applicability of the method with regard to 1) the classes of stochastic inputs for which the procedure is valid, and 2) the parameters of those processes. A Wiener or Wiener-derived white-noise process is used as the bench mark process here. Based on the results of the analysis, the introduction of certain preprocessors to extend the applicability of the procedure are suggested. View full abstract»

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  • Pattern Recognition from Satellite Altitudes

    Publication Year: 1968 , Page(s): 38 - 47
    Cited by:  Papers (12)
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    Several decision algorithms were used to classify complex patterns recorded by TV cameras aboard unmanned, scientific satellites. Recognition experiments were performed with two kinds of patterns: lunar topographic features and clouds in the earth's atmosphere. Classification accuracies ranged from 53 percent to 99 percent on independent data. View full abstract»

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  • Testing the NORAD Command and Control System

    Publication Year: 1968 , Page(s): 47 - 51
    Cited by:  Papers (1)
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    This paper describes closed-loop testing of the NORAD Command and Control System's ability to perform its assigned task: provide support for CINCNORAD in directing the air defense of North America. Special techniques were required to perform this testing since human beings were an integral part of the feedback loop. System test design and results are treated. View full abstract»

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  • On Expediency and Convergence in Variable-Structure Automata

    Publication Year: 1968 , Page(s): 52 - 60
    Cited by:  Papers (33)
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    A stochastic automaton responds to the penalties from a random environment through a reinforcement scheme by changing its state probability distribution in such a way as to reduce the average penalty received. In this manner the automaton is said to possess a variable structure and the ability to learn. This paper discusses the efficiency of learning for an m-state automaton in terms of expediency and convergence, under two distinct types of reinforcement schemes: one based on penalty probabilities and the other on penalty strengths. The functional relationship between the successive probabilities in the reinforcement scheme may be either linear or nonlinear. The stability of the asymptotic expected values of the state probability is discussed in detail. The conditions for optimal and expedient behavior of the automaton are derived. Reduction of the probability of suboptimal performance by adopting the Beta model of the mathematical learning theory is discussed. Convergence is discussed in the light of variance analysis. The initial learning rate is used as a measure of the overall convergence rate. Learning curves can be obtained by solving nonlinear difference equations relating the successive expected values. An analytic expression concerning the convergence behavior of the linear case is derived. It is shown that by a suitable choice of the reinforcement scheme it is possible to increase the separation of asymptotic state probabilities. View full abstract»

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  • Evolution of Heuristics by Human Operators in Control Systems

    Publication Year: 1968 , Page(s): 60 - 71
    Cited by:  Papers (2)
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    This paper presents a mathematical model for decision making in control systems. The model is constructed to perform four modes of control: 1) probing, 2) gradient, 3) heuristic, and 4) terminal. The operation of the model switches from one mode to another by following certain decision logic, which simulates the function of a human operator in a control system and the evolution of heuristics for control. The simulation results compare favorably with the data obtained from experiments with subjects. View full abstract»

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  • Dynamical Characteristics of the Fusional Vergence Eye-Movement System

    Publication Year: 1968 , Page(s): 72 - 79
    Cited by:  Papers (9)
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    Experiments have been performed on the fusional vergence eye-movement mechanism in humans to provide a comparison with the established dynamical characteristics of the versional eye-movement system. Transient and frequency response experiments indicate that the fusional vergence system is not characterized by sampled data or refractory operation. When provided with a periodic input, this system may utilize prediction to reduce inherent phase lags. The gain of the system, although apparently unaffected by the predictive mechanism, is subject to input amplitude-dependent nonlinearities. Under conditions of artificially high loop gain, the system breaks into smooth sustained oscillations at a frequency predicted by frequency response data. The absence of a refractory period in the fusional vergence system is demonstrated by the system response to brief pulsatile stimulation. These results are discussed, emphasizing comparison with corresponding results from experiments on the versional system. View full abstract»

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  • Contributors

    Publication Year: 1968 , Page(s): 80 - 82
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  • Book Reviews

    Publication Year: 1968 , Page(s): 83 - 84
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  • Information for authors

    Publication Year: 1968 , Page(s): 84b
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