## Understanding Social Feedback in Biological Collectives with Smoothed Model Checking

2022
##### Publication type
Contribution to a conference collection
Published
##### Published in
Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III / Margaria, Tiziana; Steffen, Bernhard (ed.). - Cham : Springer, 2022. - (Lecture Notes in Computer Science ; 13703). - pp. 181-198. - ISSN 0302-9743. - eISSN 1611-3349. - ISBN 978-3-031-19758-1
##### Abstract
Biological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback - how the collective behaviour adapts to changes in the group size - requires dealing with complex models and limited experimental data. In this work, we assume that the collective response is experimentally observed for a chosen, finite set of group sizes. Based on such data, we propose a framework which allows to: (i) predict the collective response for any given group size, and (ii) automatically propose a fitness function. We use Smoothed Model Checking, an approach based on Gaussian Process Classification, to develop a methodology that is scalable, flexible, and data-efficient; We specify the fitness function as a template temporal logic formula with unknown parameters, and we automatically infer the missing quantities from data. We evaluate the framework over a case study of a collective stinging defence mechanism in honeybee colonies.
##### Subject (DDC)
004 Computer Science
##### Keywords
Social feedback, Gaussian processes, Biological collectives, Smoothed model checking
##### Conference
ISoLA 2022 : Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, Oct 22, 2022 - Oct 30, 2022, Rhodes, Greece
##### Cite This
ISO 690KLEIN, Julia, Tatjana PETROV, 2022. Understanding Social Feedback in Biological Collectives with Smoothed Model Checking. ISoLA 2022 : Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Rhodes, Greece, Oct 22, 2022 - Oct 30, 2022. In: MARGARIA, Tiziana, ed., Bernhard STEFFEN, ed.. Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III. Cham:Springer, pp. 181-198. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-19758-1. Available under: doi: 10.1007/978-3-031-19759-8_12
BibTex
@inproceedings{Klein2022-10-17Under-59812,
year={2022},
doi={10.1007/978-3-031-19759-8_12},
title={Understanding Social Feedback in Biological Collectives with Smoothed Model Checking},
number={13703},
isbn={978-3-031-19758-1},
issn={0302-9743},
publisher={Springer},
series={Lecture Notes in Computer Science},
booktitle={Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III},
pages={181--198},
editor={Margaria, Tiziana and Steffen, Bernhard},
author={Klein, Julia and Petrov, Tatjana}
}

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<dcterms:abstract xml:lang="eng">Biological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback - how the collective behaviour adapts to changes in the group size - requires dealing with complex models and limited experimental data. In this work, we assume that the collective response is experimentally observed for a chosen, finite set of group sizes. Based on such data, we propose a framework which allows to: (i) predict the collective response for any given group size, and (ii) automatically propose a fitness function. We use Smoothed Model Checking, an approach based on Gaussian Process Classification, to develop a methodology that is scalable, flexible, and data-efficient; We specify the fitness function as a template temporal logic formula with unknown parameters, and we automatically infer the missing quantities from data. We evaluate the framework over a case study of a collective stinging defence mechanism in honeybee colonies.</dcterms:abstract>