Constructivist learning based on multiagent systems. An application to the complex problem of cooperative traffic regulation

Constructivist learning based on multiagent systems. An application to the complex problem of cooperative traffic regulation

Maxime Guériau Frédéric Armetta Salima Hassas Romain Billot Nour-Eddin El Faouzi  

Enable – CONNECT Research Centre, School of Computer Science and Statistics Trinity College, Dublin.

Univ. Lyon, UMR CNRS 5205 LIRIS, F-69622 Villeurbanne, France

LICIT, Univ. Lyon – IFSTTAR, LICIT, F-69675 Bron, France, ENTPE, LICIT, F-69518 Vaulx En Velin, France

IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, F-29238 Brest, France

Corresponding Author Email: 
maxime.gueriau@scss.tcd.ie; {frederic.armetta,salima.hassas}@univ-lyon1.fr; nour-eddin.elfaouzi@ifsttar.fr; romain.billot@imt-atlantique.fr
Page: 
249-277
|
DOI: 
https://doi.org/10.3166/RIA.32.249-277
Received: 
|
Accepted: 
|
Published: 
30 April 2018
| Citation

OPEN ACCESS

Abstract: 

Decision making in autonomous systems is particularly challenging in unknown and changing complex environments, where providing a complete a priori representation is not possible. The so built representation should be the result of the system interactions with the environment. To illustrate the problem, we consider a decentralized control of road traffic, where a control device of the distributed infrastructure locally controls traffic by sending recommendation messages to connected vehicles. We propose an approach able to combine, without prior domain-knowledge, a set of existing traditional unsupervised learning methods that collaborate as a population of agents in order to build an efficient representation. This study addresses the main scientific issues to consider for such a system to efficiently learn. Our approach follows a constructivist learning perspective, where a population of agents is able to collectively build a representation that dynamically combines discretization processes.  

Keywords: 

constructivist learning, decision-making, control

1. Contexte
2. Contributions
3. Contrôle et régulation du trafic
4. Vers une approche constructiviste pour le contrôle des C-ITS
5. Construction d’une représentation par apprentissage concurrent
6. Application : stratégie I2V de contrôle des C-ITS
7. Conclusion et perspectives
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