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Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks

Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a cert...

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Autores principales: Rausch, Ilja, Simoens, Pieter, Khaluf, Yara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805822/
https://www.ncbi.nlm.nih.gov/pubmed/33501253
http://dx.doi.org/10.3389/frobt.2020.00086
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author Rausch, Ilja
Simoens, Pieter
Khaluf, Yara
author_facet Rausch, Ilja
Simoens, Pieter
Khaluf, Yara
author_sort Rausch, Ilja
collection PubMed
description Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.
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spelling pubmed-78058222021-01-25 Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks Rausch, Ilja Simoens, Pieter Khaluf, Yara Front Robot AI Robotics and AI Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss. Frontiers Media S.A. 2020-07-09 /pmc/articles/PMC7805822/ /pubmed/33501253 http://dx.doi.org/10.3389/frobt.2020.00086 Text en Copyright © 2020 Rausch, Simoens and Khaluf. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Rausch, Ilja
Simoens, Pieter
Khaluf, Yara
Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title_full Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title_fullStr Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title_full_unstemmed Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title_short Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks
title_sort adaptive foraging in dynamic environments using scale-free interaction networks
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805822/
https://www.ncbi.nlm.nih.gov/pubmed/33501253
http://dx.doi.org/10.3389/frobt.2020.00086
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