File:Evolution-of-Collective-Behaviour-in-an-Artificial-World-Using-Linguistic-Fuzzy-Rule-Based-Systems-pone.0168876.s005.ogv
Summary
Description |
English: Video sequence portraying a representative of the evolved polarized behaviour (evolution no. 12). Polarization is very high and momentum low throughout the entire simulation run and the evolved behaviour can be classified as polarized behaviour. Note that the local density is higher than in the case when prey behaviour evolved with no predator present (see S1 Video). Prey agents learned to stay inside the borders of the living area, to avoid each other (prevent collisions) and group (by matching each other’s heading). Prey agents learned also to react to predator attacks (see frames 1800–2000, 2700–2900, 3600–3900 and 4500–5100). Note that soon after the disturbances induced by the predator attacks the polarized behaviour re-stabilizes. Note also that apart from one individual all other prey agents resort to grouping and polarized behaviour, whereas the aforementioned individual does so only occasionally, evidence that in our case the behaviours of prey agents are heterogeneous (see frames 900–2700). For this reason the individual, however, becomes an easy target for the predator that attacks peripheral prey (see frames 2700–2900). |
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Date | |||
Source | S4 Video from Demšar J, Lebar Bajec I (2017). "Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems". PLOS ONE. DOI:10.1371/journal.pone.0168876. PMID 28045964. PMC: 5207603. | ||
Author | Demšar J, Lebar Bajec I | ||
Permission (Reusing this file) |
This file is licensed under the Creative Commons Attribution 4.0 International license.
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Category:Agent-based modeling
Category:CC-BY-4.0
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