Les Systèmes Multi-Agents (SMA) ont montré depuis plusieurs années leur adéquation à modéliser et simuler les systèmes complexes. Nous suivons cette approche pour modéliser une colonie d’abeilles située dans une ruche Dadant, où plusieurs dizaines de milliers d’individus interagissent, dans le but d’évaluer l’impact d’actions locales au niveau des abeilles (e.g. pratiques apicoles) sur la colonie. Nous nous concentrons ici sur l’activité de butinage, en nous intéressant plus particulièrement au phénomène d’auto-organisation qui conduit les butineuses à sélectionner les meilleures sources de nourriture disponibles. Les interactions des butineuses avec l’environnement extérieur de la ruche, qui diffère de l’intérieur en termes de granularité des actions et d’échelle, sont simulées grâce à un module paramétrable et compatible agent, en fonction de la météo et des sources de nourriture environnantes. Les résultats de deux expérimentations du modèle, l’une sur une année complète, et l’autre sur une journée, montrent que le phénomène d’auto-organisation des butineuses résulte du comportement des butineuses et des mécanismes de recrutement implantés, et offrent une première validation de notre modèle.
The agent-based approach has been successfully used in the past years to model and simulate complex systems. We use this approach on a honeybee colony in a Dadant hive, where several tens of thousands of bees interact, in order to evaluate the impact of local actions at the bee-level (such as beekeeping practices) on the global system. In this article, we focus on the foraging activity, its self-organisation mechanisms and the behaviour of foraging bees, and how these bees interact with the environment of the hive, greatly different in granularity and scale. We present a customizable, agent-compliant module that aims at modelling and simulating the foraging, according to the weather and the surrounding nectar sources. The results of two experimentations provide a first validation of our model, showing that the agents’ behaviours lead to a self-organizing process of the best available sources’ selection.
Révisé le :
Accepté le :
Publié le :
Mots clés : Multiagent simulation, Self-organization, Environment, Complex Systems
CC-BY 4.0
@article{ROIA_2022__3_5-6_423_0,
author = {J\'er\'emy Rivi\`ere and Thomas Alves and C\'edric Alaux and Yves Le Conte and Yves Layec and Andr\'e Lozac{\textquoteright}h and Frank Singhoff and Vincent Rodin},
title = {Mod\`ele multi-agent d{\textquoteright}auto-organisation pour le butinage au sein d{\textquoteright}une colonie d{\textquoteright}abeilles},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {423--450},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {3},
number = {5-6},
year = {2022},
doi = {10.5802/roia.38},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.38/}
}
TY - JOUR AU - Jérémy Rivière AU - Thomas Alves AU - Cédric Alaux AU - Yves Le Conte AU - Yves Layec AU - André Lozac’h AU - Frank Singhoff AU - Vincent Rodin TI - Modèle multi-agent d’auto-organisation pour le butinage au sein d’une colonie d’abeilles JO - Revue Ouverte d'Intelligence Artificielle PY - 2022 SP - 423 EP - 450 VL - 3 IS - 5-6 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.38/ DO - 10.5802/roia.38 LA - fr ID - ROIA_2022__3_5-6_423_0 ER -
%0 Journal Article %A Jérémy Rivière %A Thomas Alves %A Cédric Alaux %A Yves Le Conte %A Yves Layec %A André Lozac’h %A Frank Singhoff %A Vincent Rodin %T Modèle multi-agent d’auto-organisation pour le butinage au sein d’une colonie d’abeilles %J Revue Ouverte d'Intelligence Artificielle %D 2022 %P 423-450 %V 3 %N 5-6 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.38/ %R 10.5802/roia.38 %G fr %F ROIA_2022__3_5-6_423_0
Jérémy Rivière; Thomas Alves; Cédric Alaux; Yves Le Conte; Yves Layec; André Lozac’h; Frank Singhoff; Vincent Rodin. Modèle multi-agent d’auto-organisation pour le butinage au sein d’une colonie d’abeilles. Revue Ouverte d'Intelligence Artificielle, Volume 3 (2022) no. 5-6, pp. 423-450. doi : 10.5802/roia.38. https://roia.centre-mersenne.org/articles/10.5802/roia.38/
[1] , 18th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2020) (Lecture Notes in Artificial Intelligence) (2020) | DOI | HAL
[2] Immersive and interactive visualisation of a virtual honey bee colony, EuroVR 2019 (EuroVR 2019), 2019 (Poster) | DOI | HAL
[3] et al. BEEHAVE : a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure, Journal of Applied Ecology, Volume 51 (2014) no. 2, pp. 470-482 | DOI
[4] Jade — A Java Agent Development Framework, Multi-Agent Programming : Languages, Platforms and Applications (Rafael H. Bordini; Mehdi Dastani et al., eds.), Springer US, 2005, pp. 125-147 | DOI
[5] Bee++ : An Object-Oriented, Agent-Based Simulator for Honey Bee Colonies, Insects, Volume 8 (2017) no. 1, 31 | DOI
[6] Agent-based modeling : Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences, Volume 99 (2002) no. 3, pp. 7280-7287 | DOI
[7] et al. Self-organization in social insects, Trends in Ecology and Evolution, Volume 12 (1997) no. 5, pp. 188-193 | DOI
[8] , Agent Based Simulation (2001), pp. 18-23
[9] et al., From Agent Theory to Agent Implementation (AT2AI’04) (2004), pp. 576-581
[10] et al., Advances in Practical Applications of Cyber-Physical Multi-Agent Systems : The PAAMS Collection (2017), pp. 31-42 | DOI
[11] et al. Benefits of recruitment in honey bees : effects of ecology and colony size in an individual-based model, Behavioral Ecology, Volume 17 (2006) no. 3, pp. 336-344 | DOI
[12] De la simulation multi-agents a la resolution collective de problèmes : une étude de l’émergence de structures d’organisation dans les systèmes multi-agents, Ph. D. Thesis, Paris 6 (1993), 382 pages http://www.theses.fr/1993PA066544 (Dirigée par J. Ferber, J. Sciences appliquées)
[13] Multi-Agent Systems and Simulation : A Survey from the Agent Community’s Perspective, Multi-Agent Systems – Simulation and Applications (Adelinde M. Uhrmacher; Danny Weyns, eds.) (Computational Analysis, Synthesis, and Design of Dynamic Systems), CRC Press / Taylor and Francis, 2009, pp. 3-51 | DOI
[14] Couplage de modèles à l’aide d’agents : le système OSIRIS, Ph. D. Thesis, Universtié Paris IX-Dauphine (2001)
[15] Speech acts library, Web, 2006
[16] Honey Bee Nest Thermoregulation : Diversity Promotes Stability, Science, Volume 305 (2004) no. 5682, pp. 402-404 | DOI
[17] , 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009) (2009), pp. 100-109 | DOI
[18] IODA : an interaction-oriented approach for multi-agent based simulations, Autonomous Agents and Multi-Agent Systems, Volume 23 (2011) no. 3, pp. 303-343 | DOI
[19] Complexity : A Guided Tour, Oxford University Press, 2009
[20] Dynamic modelling of honey bee (Apis mellifera) colony growth and failure, Ecological Modelling, Volume 265 (2013), pp. 158-169 | DOI
[21] HoPoMo : A model of honeybee intracolonial population dynamics and resource management, Ecological Modelling, Volume 204 (2007) no. 1, pp. 219-245 | DOI
[22] Swarm-intelligent foraging in honeybees : benefits and costs of task-partitioning and environmental fluctuations, Neural Computing and Applications, Volume 21 (2012) no. 2, pp. 251-268 | DOI
[23] Division of labor between scouts and recruits in honeybee foraging, Behavioral Ecology and Sociobiology, Volume 12 (1983) no. 3, pp. 253-259 | DOI
[24] Honey bee foragers as sensory units of their colonies, Behavioral Ecology and Sociobiology, Volume 34 (1994) no. 1, pp. 51-62 | DOI
[25] The Wisdom of the Hive, Harvard University Press, 1995 | DOI
[26] Collective decision-making in honey bees : how colonies choose among nectar sources, Behavioral Ecology and Sociobiology, Volume 28 (1991) no. 4, pp. 277-290 | DOI
[27] Assessing the benefits of cooperation in honeybee foraging : search costs, forage quality, and competitive ability, Behavioral Ecology and Sociobiology, Volume 22 (1988) no. 4, pp. 229-237 | DOI
[28] , Multi-Agent Systems and Agent-Based Simulation (1998), pp. 95-110 | DOI
[29] Shape and Dynamics of Thermoregulating Honey Bee Clusters, Journal of Theoretical Biology, Volume 204 (2000) no. 1, pp. 1-14 | DOI
[30] , Multi-Agent Systems and Applications IV (2005), pp. 246-255 | DOI
[31] Queen and young larval pheromones impact nursing and reproductive physiology of honey bee (Apis mellifera) workers, Behavioral Ecology and Sociobiology, Volume 68 (2014) no. 12, pp. 2059-2073 | DOI
[32] Multi-Agent Systems and Simulation : A Survey from an Application Perspective, Multi-Agent Systems – Simulation and Applications (Adelinde M. Uhrmacher; Danny Weyns, eds.) (Computational Analysis, Synthesis, and Design of Dynamic Systems), CRC Press / Taylor and Francis, 2009, pp. 53-75 | DOI
[33] Modelling collective foraging by means of individual behaviour rules in honey-bees, Behavioral Ecology and Sociobiology, Volume 44 (1998) no. 2, pp. 109-124 | DOI
[34] NetLogo (1999) http://ccl.northwestern.edu/netlogo/ (http ://ccl.northwestern.edu/netlogo/)
[35] The Biology of the Honey Bee, Harvard University Press, 1987
Cité par Sources :