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Poster Communications Year : 2021

Automatic Modeling of Dynamical Interactions Within Marine Ecosystems

Abstract

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.
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Dates and versions

hal-03347033 , version 1 (16-09-2021)

Identifiers

  • HAL Id : hal-03347033 , version 1

Cite

Omar Iken, Maxime Folschette, Tony Ribeiro. Automatic Modeling of Dynamical Interactions Within Marine Ecosystems. 1st International Joint Conference on Learning & Reasoning, Oct 2021, (virtual), Greece. . ⟨hal-03347033⟩
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