Le Fur Jean, Mboup P.A., Sall M. (2023). Growing bioinspired synthetic landscape ecologies and the adequacy of object oriented programming. In :
Wagner G. (ed.), Werner F. (ed.), Oren T. (ed.), De Rango F. (ed.). Simulation and modeling methodologies, technologies and applications : International Online Conference (SIMULTECH 2021). 118-137. (Lecture Notes in Networks and Systems ; 601). SIMULTECH : International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 11., [En Ligne], 2021/07/07-09. ISBN 978-3-031-23148-3.
Titre du document
Growing bioinspired synthetic landscape ecologies and the adequacy of object oriented programming
Année de publication
2023
Type de document
Partie d'ouvrage
Auteurs
Le Fur Jean, Mboup P.A., Sall M.
In
Wagner G. (ed.), Werner F. (ed.), Oren T. (ed.), De Rango F. (ed.) Simulation and modeling methodologies, technologies and applications : International Online Conference (SIMULTECH 2021)
Source
2023,
118-137 (Lecture Notes in Networks and Systems ; 601). ISBN 978-3-031-23148-3
Colloque
SIMULTECH : International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 11., [En Ligne], 2021/07/07-09
In this study we develop, using basic object-oriented paradigms, and in collaboration with biologists, a comprehensive model of landscapes and ecosystems dynamics based on bioinspiration principles. Faced with the issue of taking into consideration a variety of elements, processes, interactions, contexts, and scales simultaneously effective, we iteratively develop this model using successive aggregation of new components based on specific case studies. These were then generalized and consolidated to form a coherent platform. To address robustness, the model was continually reworked in search of the closest resemblance to the concrete workings of Nature.
We have arrived at a general architecture built from the bottom up that is both generic and as parsimonious as possible. The model emerging from this compilation is a shared class tree with three primary categories of variability: (i) cognitive living agents, (ii) containers of agents that can be nested at various functional scales, and (iii) particular genomes that instantiate attributes for each type of agent. The results of the iterative strategy to modeling synthetic ecology are discussed, as well as the suitability of object-oriented paradigms (composition, aggregation, inheritance, generalization...) for achieving the goal of bioinspired modeling.
Plan de classement
Sciences du milieu [021]
;
Etudes, transformation, conservation du milieu naturel [082]
;
Informatique [122]
Localisation
Fonds IRD [F B010094649]
Identifiant IRD
fdi:010094649
