Posts tagged "ia"

ECONECT: Developing connected environmental sentinel systems to better understand the degradation of rivers, the decline of bees and birds

Context Presentation

The ECONECT project began in early 2020, with the objective to develop a communication infrastructure allowing the remote monitoring of autonomous, connected, and versatile systems to measure the responses of bioindicator organisms to chemical contamination, habitat degradation and global warming.

Three sentinel systems are considered:(1) the connected hive, allowing to monitor the dynamics of bee colonies (colony mass, temperature and location of the bee cluster, foraging traffic, etc.) and the cognitive capacities of bees; (2) the connected bird-feeder to submit individually monitored tits to behavioral tests to assess their cognitive abilities; (3) the aquacosm, a floating enclosure allowing the measurement of eco-markers in an aquatic environment (growth dynamics of phototrophic biofilms, relative importance of autotrophic and heterotrophic processes within the ecosystem ...).

In 2022, a network of 12 sentinel stations will be deployed in the Zone Atelier Pyrénées-Garonne (PYGAR). Each station will be characterized by a spatial analysis of land use and the quality of habitats and by the measurement of concentrations of chemical contaminants (trace metal elements, PAHs, pesticides) in different compartments of the environment. Participatory science protocols will be used to supplement the available data set and to assist in the interpretation of observed trends, while providing environmental education opportunities for the public.

schema (EN) - Arnaud Elger

Keywords

Environmental sensor; Bioindicator; Animal cognition; Chemical status; Landscape integrity; Artificial intelligence

Scientific goals

•    to design a communicating infrastructure to collect data from different sensors in the field;

•    to develop automated tools for the real-time analysis of collected data, for extracting their ecological significance;

•    to examine the relevance of our sentinel systems to assess the quality of the environment, particularly in terms of chemical status and landscape integrity.

Contact

arnaud.elger@univ-tlse3.fr

Endogenous Learning by Cooperation

Context Presentation

The current digital transformation requires the creation of autonomous applications that can be adapted to complex, dynamic, heterogeneous and unpredictable environments. These systems must be equipped with proactive learning capabilities. To this end, Self-Adaptive Multi-Agent principle allows the decentralization and self-observation of the learning process. Each knowledge granule is an autonomous agent that cooperates with its neighbors to improve learning from exogenous and endogenous feedbacks. Detecting and solving concurrences, conflicts and incompetencies leads to active endogenous learning.This work on an adaptive decentralized learning mechanism will be applied on application domains such as robotics, autonomous vehicles and smart cities.

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Figure 1: « Schema of the Learning

Figure 2: « Implementation Example Multi-Agent System » on an Industrial Robot »

Scientific Goals

- Design a Self-Learning System

- Lifelong and Endogenous Learning

- Genericity and Scalability

Keywords

Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Artificial Intelligence

Contacts

bruno.dato@irit.frfrederic.migeon@irit.fr marie-pierre.gleizes@irit.fr

Smart Twins

L’objectif de ce projet est de développer une intelligence artificielle basée sur le paradigme multi-agent afin de contrôler des environnements peuplés de capteurs et d’effecteurs pour maximiser le confort des utilisateurs. Différentes contraintes sont à prendre en compte afin de résoudre ce problème comme le traitement des données en temps réel et l’adaptation du système aux différentes situations rencontrées. Les principaux défis liés à ce projet sont l’incapacité de prédire à l’avance l’ensembles des situations que le système va rencontrer et la capacité du système à s’adapter en temps réel aux utilisateurs. De manière plus générale le système devra être capable de s’adapter aux utilisateurs et d’apprendre leurs préférences.

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Figure 1 : « Mécanisme d’apprentissage multi-agent »

Objectifs scientifiques

Les objectifs de ce projet sont :

 De créer un système capable d’améliorer le confort des utilisateurs

 De créer un système multi-agent auto adaptatif qui ne nécessite aucune connaissance préalable

 De contrôler en temps réel un environnement connecté

Contacts

thomas.gandilhon@irit.fr

marie-pierre.gleizes@irit.fr

patrick.marquet@sogeti.com

 

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