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Couplage de simulations multi-agents pour la conception de politiques urbaines
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| Simon Pageaud, Véronique Deslandres, Salima Hassas, Vassilissa Lehoux |
| Journées Francophones sur les Systèmes Multi-Agents (JFSMA), Métabief, France, 10-12 October, 2018 |
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@inproceedings{DBLP:conf/jfsma/PageaudDHL18,
author = {Simon Pageaud and
V{\'{e}}ronique Deslandres and
Salima Hassas and
Vassilissa Lehoux},
title = {Couplage de simulations multi-agents pour la conception de politiques
urbaines (pr{\'{e}}sentation courte)},
booktitle = {Distribution et D{\'{e}}centralisation - Vingt-sixi{\`{e}}mes
journ{\'{e}}es francophones sur les syst{\`{e}}mes multi-agents,
{JFSMA} 2018, M{\'{e}}tabief, France, 10-12 Octobre 2018.},
pages = {211--220},
year = {2018},
crossref = {DBLP:conf/jfsma/2018},
}In the near future, the increasing availability of data will require policy makers to regularly change urban policies to incorporate changing behavior and user feedbacks. In this paper, we propose a generic agent-based architecture for designing and modeling urban policies in order to test their relevance by deploying them on a specific environment. Environments are designed using data from the city provided by any available open data toolkit (Open Street Map here). Two agent-based models are coupled in a micro-macro dynamic loop and they can be adapted either by the system using reinforcement learning or by the stakeholders using simulation results. We propose a formalism to represent urban policies to allow a co-designed iterative process between the policymaker and our system. An experimentation on the regulation of parking prices in downtown area justifies the use of our architecture to design relevant urban policies.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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