Abstract
Lizhi Wang, Vassilissa Lehoux, Marie-Laure Espinouse, Van-Dat Cung |
Open Repositories (OR), Brussels, Belgium, 12-14 September, 2018 |
Abstract
The size of the Pareto alternative set of a multicriteria multimodal shortest path problem can be exponential. Even approximation sets can remain quite large, while a user of multimodal trip planner app would like to have only a few choices but those choices must be relevant for him. Therefore, we need to process a selection of alternatives before proposing them to the users. Furthermore, since users can have different preferences over the characteristics of an itinerary, the set of alternative needs to be personalized to the user. By consequence, this study started by wondering: How can we propose a reasonable small size set of “personally good” alternatives to the corresponding user? For this aim, we need to find a way to model the users’ preferences, and then evaluate each alternative comparing to the others regarding the user’s preferences. In the existing studies, the users’ preferences are mostly modelled by some weights for each criterion, which are given by the users and that enable to aggregate all the criteria into one single objective function. We think it’s difficult to describe one person’s feeling by a crisp number and that the Fuzzy logic is better suited in this situation. In this talk, we are proposing a Fuzzy system to rank the alternatives. Our system has three phases. In the first phase, we transform the users’ preferences and the alternatives’ criteria evaluation into fuzzy sets. For example, one user’s preference on the subways can be “Like” with a descriptive membership value, the total duration of one alternative can be “Long” with its membership value. During the alternatives’ criteria transformation, we are proposing two novel methods of fuzzification according to the different types of criteria for setting the membership functions. The first method uses a parameter which is called the “tolerance” of the user for each criterion, while the second method is based on a clustering of the alternatives. In the second phase, a combination between user’s preferences and the rating of the alternatives for each criterion according to the membership functions allows to evaluate the user’s satisfaction on each criterion. The combination is realized by using a set of fuzzy rules. In the third phase, we are proposing a new defuzzification method, combining the different level of satisfactions that we calculated in the second phase into a general satisfaction of the alternative. The general satisfaction is represented by a crisp number that is our ranking parameter. In order to assess the method, we built a survey in which we asked people to rank alternatives, and we compared the surveys’ results with the results from our ranking system.
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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 optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
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.
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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