Entropy as a Quality Measure of Correlations between n Information Sources in Multi-agent Systems

G. Enee, J. Collonge

Abstract

Shanon’s entropy has been widely used through different Science fields, as an example, to measure the quantity of information found in a message coming from a source. In real world applications, we need to measure the quality of several crossed information sources. In the specific case of language creation within multi-agent systems, we need to measure the correlation between words and their meanings to evaluate the quality of that language. When sources of information are numerous, we are willing to make correlations between those differents sources. Considering those n sources of information are put together in a matrix having n dimensions, we propose in this paper to extend Shanon’s entropy to measure information quality in R2+ and then in Rn+.

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Paper Citation


in Harvard Style

Enee G. and Collonge J. (2019). Entropy as a Quality Measure of Correlations between n Information Sources in Multi-agent Systems.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-350-6, pages 281-287. DOI: 10.5220/0007684802810287


in Bibtex Style

@conference{icaart19,
author={G. Enee and J. Collonge},
title={Entropy as a Quality Measure of Correlations between n Information Sources in Multi-agent Systems},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2019},
pages={281-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007684802810287},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Entropy as a Quality Measure of Correlations between n Information Sources in Multi-agent Systems
SN - 978-989-758-350-6
AU - Enee G.
AU - Collonge J.
PY - 2019
SP - 281
EP - 287
DO - 10.5220/0007684802810287