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Proceedings 2017, 1(3), 194; doi:10.3390/IS4SI-2017-04037

Application of Information Theory Entropy as a Cost Measure in the Automatic Problem Solving

Department of Computer Science and Robotics Engineering Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, USA
Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.
Published: 9 June 2017
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Abstract

We study the relation between Information Theory and Automatic Problem Solving to demonstrate that the Entropy measure can be used as a special case of $-Calculus Cost Functions measure. We hypothesize that Kolmogorov Complexity (Algorithmic Entropy) can be useful to standardize $-Calculus Search (Algorithm) Cost Function.
Keywords: information theory; entropy; superTuring models of computation; automatic problem solving; $-calculus; machine learning; ID3 information theory; entropy; superTuring models of computation; automatic problem solving; $-calculus; machine learning; ID3
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Eberbach, E. Application of Information Theory Entropy as a Cost Measure in the Automatic Problem Solving. Proceedings 2017, 1, 194.

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