Automatic Modeling of Dynamical Interactions ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...)
Title :
Automatic Modeling of Dynamical Interactions Within Marine Ecosystems
Author(s) :
Iken, Omar [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Folschette, Maxime [Auteur]
BioComputing
Ribeiro, Tony [Auteur]
Méthodes Formelles pour la Bioinformatique [LS2N - équipe MéForBio]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Folschette, Maxime [Auteur]
BioComputing
Ribeiro, Tony [Auteur]
Méthodes Formelles pour la Bioinformatique [LS2N - équipe MéForBio]
Conference title :
1st International Joint Conference on Learning & Reasoning
City :
(virtual)
Country :
Grèce
Start date of the conference :
2021-10-25
English keyword(s) :
logical modeling
dynamic systems
heuristics
interaction graph
dynamic systems
heuristics
interaction graph
HAL domain(s) :
Informatique [cs]/Modélisation et simulation
Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]/Ecologie, Environnement
Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]/Ecologie, Environnement
English abstract : [en]
Marine ecology models are used to study and anticipate population variations of plankton and microalgae species. These variations can have an impact on ecological niches, the economy or the climate. Our objective is the ...
Show more >Marine ecology models are used to study and anticipate population variations of plankton and microalgae species. These variations can have an impact on ecological niches, the economy or the climate. Our objective is the automation of the creation of such models. Learning From Interpretation Transition (LFIT) is a framework that aims at learning the dynamics of a system by observing its state transitions. LFIT provides explainable predictions in the form of logical rules. In this paper, we introduce a method that allows to extract an influence graph from a LFIT model. We also propose an heuristic to improve the model against noise in the data.Show less >
Show more >Marine ecology models are used to study and anticipate population variations of plankton and microalgae species. These variations can have an impact on ecological niches, the economy or the climate. Our objective is the automation of the creation of such models. Learning From Interpretation Transition (LFIT) is a framework that aims at learning the dynamics of a system by observing its state transitions. LFIT provides explainable predictions in the form of logical rules. In this paper, we introduce a method that allows to extract an influence graph from a LFIT model. We also propose an heuristic to improve the model against noise in the data.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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