SEER, A SEquence Extrapolating Robot

Hagelbarger, D. W.

doi:10.1109/tec.1956.5219783

Cited by 17 publications

(10 citation statements)

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“…From the description above we can notice that the machine does not use the StateOfPlay-based 7 state counters (hard to implement by relays) anymore, it uses only the StateOfPlay-based 2-bit change history register. This is in line with the statement made in [1]: "C.E. Shannon has built a machine using about half as many relays which follows a simplified version of the same strategy.…”

Section: Shannon's Mind-reading Machinesupporting

confidence: 83%

“…In [1] it is stated that "After much discussion an umpire machine was built which connected the two machines, and they were allowed to play several thousand games. The agility of the small machine triumphed, and it beat the larger one about 55-45.…”

Section: Experimental Results (1) -Seer Vs Mrmmentioning

confidence: 99%

“…The Hagelbarger's SEER (SEquence Extrapolating Robot) is presented in [1]. Its following description is taken/adapted from that paper; all quotation marks refer to it.…”

Section: Hagelbarger's Seermentioning

confidence: 99%

“…Hagelbarger calls his machine "SEquence Extrapolating Robot' (SEER) [1], focusing on the feature that extrapolates the series of the opponent's play. Shannon calls his machine "Mind Reading machine" (MRM) [2], focusing on the feature that it models the opponents' mind.…”

Section: Introduction Game Descriptionmentioning

confidence: 99%

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On Hagelbarger’s and Shannon’s matching pennies playing machines

Breazu

Volovici

Morariu

et al. 2020

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences

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In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the use of Hidden Markov Model for modelling player’s behaviour.

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Section: Shannon's Mind-reading Machinesupporting

confidence: 83%

Section: Experimental Results (1) -Seer Vs Mrmmentioning

confidence: 99%

“…The Hagelbarger's SEER (SEquence Extrapolating Robot) is presented in [1]. Its following description is taken/adapted from that paper; all quotation marks refer to it.…”

Section: Hagelbarger's Seermentioning

confidence: 99%

Section: Introduction Game Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

On Hagelbarger’s and Shannon’s matching pennies playing machines

Breazu

Volovici

Morariu

et al. 2020

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences

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“…The study of sequential dependencies in behavioral data has a long history. Looking beyond the PoP task, sequentially dependent behaviour has been observed in virtually every choice task studied, including production of random data (Goodfellow, 1938, Hagelbarger, 1956, Skinner, 1942, detection of liminal signals (Senders & Sowards, 1952, Verplanck, Collier & Cotton, 1952, speeded choice among alternatives (Bertelson, 1965, Laming, 1969, magnitude estimation (DeCarlo & Cross, 1990), categorization (Stewart, Brown & Chater, 2002), sensorimotor adaptation (Baddeley, Ingram & Miall, 2003) and reward-contingent choice (Corrado, Sugrue, Seung & Newsome, 2005, Hunter & Davison, 1985, Lau & Glimcher, 2005. The list is certainly not exhaustive, but in all these instances the observed pattern of sequential dependencies can be reduced to two common types, classically known as "contrast" and "assimilation" (Treisman & Faulkner, 1984).…”

Section: Similarities Beetween Pop and Other Sequential Dependenciesmentioning

confidence: 99%

System identification in Priming of Pop-Out

Martini

2010

Vision Research

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Inter-trial repetitions of a target's features in a visual search task reduce the time needed to find the target. Here I examine these sequential dependencies in the Priming of Pop-Out task (PoP) by means of system identification techniques. The results are as follows. Response time facilitation due to repetition of the target's features increases linearly with difficulty in segmenting the target from the distracters. However, z-scoring the reaction times normalizes responses by equating facilitation across levels of difficulty. Memory kernels, representing the influence of the current trial on any future trial, can then be calculated from data normalized and averaged across conditions and observers. The average target-defining feature kernel and the target position kernel are well fit by a sum of two exponentials model, comprised of a high-gain, fast-decay component and a low-gain, slow-decay component. In contrast, the average response-defining feature kernel is well fit by a single exponential model with very low-gain and decay similar to the slow component of the target-defining feature kernel. Analysis of single participant's data reveals that a fast-decay component is often also present for the response-defining feature, but can be either facilitatory or inhibitory and thus tends to cancel out in pooled data. Overall, the results are similar to integration functions of reward history recently observed in primates during frequency-matching experiments. I speculate that sequential dependencies in PoP result from learning mechanisms that bias the attentional weighting of certain aspects of the stimulus in an effort to minimize a prediction error signal.

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Untersuchungen an Binärprädiktoren, insbesondere bezüglich ihrer Anpassungsfähigkeit und ihrer Vorhersageleistung gegenüber Versuchspersonen

Schmitt

1964

Kybernetik

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SEER, A SEquence Extrapolating Robot

Cited by 17 publications

References 0 publications

On Hagelbarger’s and Shannon’s matching pennies playing machines

On Hagelbarger’s and Shannon’s matching pennies playing machines

System identification in Priming of Pop-Out

Untersuchungen an Binärprädiktoren, insbesondere bezüglich ihrer Anpassungsfähigkeit und ihrer Vorhersageleistung gegenüber Versuchspersonen

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