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Co-construction of adaptive public policies using SmartGov
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| Simon Pageaud, Véronique Deslandres, Salima Hassas, Vassilissa Lehoux |
| 29th International Conference on Tools with Artificial Intelligence (ICTAI), Boston, USA, 6-8 November, 2017 |
| Co-construction of adaptive public policies using SmartGov.pdf |
| Download |
@inproceedings{pageaud2017co,
title={Co-construction of adaptive public policies using SmartGov},
author={Pageaud, Simon and Deslandres, V{\'e}ronique and Lehoux, Vassilissa and Hassas, Salima},
booktitle={2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)},
pages={1328--1335},
year={2017},
organization={IEEE}
}Designing a public urban policy is a demanding process which requires both time and money with no warranty of its efficiency. It involves knowledge about the purpose of urban design, behaviors of users and needs in terms of mobility. We believe that in the near future, decision makers will have to react and more frequently adapt public policies, based on the huge amount of available data, feedbacks from both target users and stakeholders. In this paper, we propose a generic agent-based architecture to model and simulate urban policies, which could facilitate the co-design and assessment of public policies in a specific environment. Two agent-based models are coupled with a micromacro dynamic loop, and they can be adapted either by the system using reinforcement learning, or by the stakeholders using simulation results. A generic formalism is elaborated to represent urban policies, which can be instantiated in a co-design approach between the policymaker and our system. An experimentation is conducted on an urban mobility policy, related to the configuration of parking price system in downtown area. The agent’s behavior and environment are developed to be as realistic as possible, based on a real-world source of modeling. However, our architecture has been designed to be generic, exploiting infrastructure data from any city using available community data (Open Street Map). The scenario of parking pricing shows that the system learns postpolicy behaviors and can propose some adjustments (e.g., specific actions to apply, when and how) to better meet the stakeholders’ objectives (e.g., maximize parking gains). The policy maker can then choose to validate the provided policies, or modified them for additional simulations.
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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