Nicolas Drougard

Nicolas Drougard
Institut Supérieur de l'Aéronautique et de l'Espace (ISAE) | ISAE · DCAS

PhD in Articifial Intelligence
Associate Professor in Artificial Intelligence at ISAE-SUPAERO

About

25
Publications
2,595
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119
Citations
Additional affiliations
October 2012 - October 2015
The French Aerospace Lab ONERA
Position
  • PhD Student

Publications

Publications (25)
Article
Full-text available
As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs-i.e., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered b...
Preprint
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available to the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations of the current state and action. Recent works showed that an expert-guided pipeline relying on Dens...
Preprint
Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space. Yet, many MDPs are endowed with invariant reward and transition functions with respect to some transformations of the current state a...
Article
Full-text available
Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a...
Article
Full-text available
This study aims at investigating the neural and physiological correlates of human-human and human-AI interactions under ecological settings. We designed a scenario in which a ground controller had to guide his/her pilot to reach a location. We also implemented a Controller-Bot and a Pilot-Bot using AI techniques to behave like real human operators....
Preprint
Offline model learning for planning is a branch of machine learning that trains agents to perform actions in an unknown environment using a fixed batch of previously collected experiences. The limited size of the data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP), bounding the performance of the obtain...
Article
Full-text available
As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations....
Preprint
The training of autonomous agents often requires expensive and unsafe trial-and-error interactions with the environment. Nowadays several data sets containing recorded experiences of intelligent agents performing various tasks, spanning from the control of unmanned vehicles to human-robot interaction and medical applications are accessible on the i...
Chapter
The recent progress in robotics and artificial intelligence raises the question of the efficient artificial agents interaction with humans. For instance, artificial intelligence has achieved technical advances in perception and decision making in several domains ranging from games to a variety of operational situations, (e.g. face recognition [51]...
Article
Full-text available
The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiat...
Article
Full-text available
Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the "brain at work" in complex real-life situations such as while operating aircraft. However, there...
Conference Paper
Full-text available
Missions involving humans interacting with automated systems become increasingly common. Due to the non-deterministic behavior of the human and possibly high risk of failing due to human factors, such an integrated system should react smartly by adapting its behavior when necessary. A promise avenue to design an efficient interaction-driven system...
Article
Full-text available
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on second-class sampling. This work fills this gap by proposing a natural methodology to extend standard splitting crite...
Article
Possibilistic and qualitative Partially Observable Markov Decision Processes (π-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief and the probabilities defining the problem are imprecise due to lack of past experiences or insufficient data collection. However, like probabilistic POMDPs, optimally solving π...
Thesis
Full-text available
Les Processus Décisionnels de Markov Partiellement Observables (PDMPOs) permettent demodéliser facilement les problèmes probabilistes de décision séquentielle dans l’incertain. Lorsqu’il s’agit d’une mission robotique, les caractéristiques du robot et de son environnement nécessaires à la définition de la mission constituent le système. Son état n’...
Conference Paper
A new translation from Partially Observable MDP into Fully Observable MDP is described here. Unlike the classical translation, the resulting problem state space is finite, making MDP solvers able to solve this simplified version of the initial partially observable problem: this approach encodes agent beliefs with possibility distributions over stat...
Conference Paper
Full-text available
Qualitative Possibilistic Mixed-Observable MDPs (π-MOMDPs), generalizing π-MDPs and π-POMDPs, are well-suited models to planning under uncertainty with mixed-observability when transition, observation and reward functions are not precisely known and can be qualitatively described. Functions denning the model as well as intermediate calculations are...
Article
Full-text available
Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection. However, like probabilistic POMDPs, optimally solving pi-POMDPs is intractable: the finite belief state spac...
Conference Paper
Possibilistic and qualitative POMDPs (π-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection. However, like probabilistic POMDPs, optimally solving π-POMDPs is intractable: the finite belief state space...
Thesis
Full-text available
This Master's thesis in Mathematics is organized around the various research works carried out under the supervision of Sylvain PEYRONNET, Thanh Mai PHAM NGOC, Sophie LAPLANTE and Rémi MUNOS. These research works are in french. Key words: Mathematics, Computer Science, Probability, Statistics, Control and optimization, Complexity, Discrete Mathemat...

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