1. bookVolume 10 (2020): Issue 1 (December 2020)
Journal Details
License
Format
Journal
eISSN
2067-354X
First Published
30 Jul 2019
Publication timeframe
2 times per year
Languages
English
access type Open Access

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

Published Online: 24 Dec 2020
Volume & Issue: Volume 10 (2020) - Issue 1 (December 2020)
Page range: 56 - 66
Journal Details
License
Format
Journal
eISSN
2067-354X
First Published
30 Jul 2019
Publication timeframe
2 times per year
Languages
English
Abstract

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.

Keywords

[1] DW Hagelbarger. Seer, A SEquence Extrapolating Robot, IRE Transactions on Electronic Computers, page 1–7, March, 1956.10.1109/TEC.1956.5219783Search in Google Scholar

[2] Claude E Shannon. A Mind-Reading(?) Machine, Bell Laboratories Memorandum, March 18, 1953.Search in Google Scholar

[3] Matthew V. Mahoney. Adaptive Weighing of Context Models for Lossless Data Compression, Florida Institute of Technology CS Dept. Technical Report CS-2005-16, 2005.Search in Google Scholar

[4] Lawrence R. Rabiner, A tutorial on HMM and selected applications in Speech Recognition, Proceedings of the IEEE, vol 77, no. 2, 1989.10.1109/5.18626Search in Google Scholar

[5] Arpad Gellert, Adrian Florea. Web prefetching through efficient prediction by partial matching, World Wide Web, volume 19, pages 921–932, 2016.10.1007/s11280-015-0367-8Search in Google Scholar

[6] https://en.wikipedia.org/wiki/Matching_penniesSearch in Google Scholar

[7] https://cs.stanford.edu/people/eroberts/courses/soco/projects/1999-00/information-theory/ai.htmlSearch in Google Scholar

[8] http://william-poundstone.com/blog/2015/7/30/how-i-beat-the-mind-reading-machineSearch in Google Scholar

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