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Authors: Sven Tomforde 1 ; Jan Kantert 2 and Bernard Sick 1

Affiliations: 1 University of Kassel, Germany ; 2 Leibniz Universität Hannover, Germany

ISBN: 978-989-758-219-6

Keyword(s): Self-organisation, Quantification of Self-organisation, Organic Computing, Adaptivity, Probabilistic Models, Communication Patterns.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Self Organizing Systems

Abstract: The term “self-organisation” typically refers to the ability of large-scale systems consisting of numerous autonomous agents to establish and maintain their structure as a result of local interaction processes. The motivation to develop systems based on the principle of self-organisation is to counter complexity and to improve desired characteristics, such as robustness and context-adaptivity. In order to come up with a fair comparison between different possible solutions, a prerequisite is that the degree of self-organisation is quantifiable. Even though there are some attempts in literature that try to approach such a measure, there is none that is real-world applicable, covers the entire runtime process of a system, and considers agents as blackboxes (i.e. does not require internals about status or strategies). With this paper, we introduce a concept for such a metric that is based on external observations, neglects the internal behaviour and strategies of autonomous entities, and provides a continuous measure that allows for an easy comparibility. (More)

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Paper citation in several formats:
Tomforde, S.; Kantert, J. and Sick, B. (2017). Measuring Self-organisation at Runtime - A Quantification Method based on Divergence Measures.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 96-106. DOI: 10.5220/0006240400960106

@conference{icaart17,
author={Sven Tomforde. and Jan Kantert. and Bernard Sick.},
title={Measuring Self-organisation at Runtime - A Quantification Method based on Divergence Measures},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={96-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006240400960106},
isbn={978-989-758-219-6},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Measuring Self-organisation at Runtime - A Quantification Method based on Divergence Measures
SN - 978-989-758-219-6
AU - Tomforde, S.
AU - Kantert, J.
AU - Sick, B.
PY - 2017
SP - 96
EP - 106
DO - 10.5220/0006240400960106

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