13 citations · 20 across the 9 of their papers we have counts for
9 papers
Interface Homme-Machine pour l'Identification des Liaisons de Coins
Patrice Labedan, Nicolas Drougard
ACCADIL is a project that led to the development of software tools for the identification of coin die links from coin photographs. It provides a computational algorithm based on co…
A High-Accuracy SSIM-based Scoring System for Coin Die Link Identification
Patrice Labedan, Nicolas Drougard, Alexandre Berezin +2
The analyses of ancient coins, and especially the identification of those struck with the same die, provides invaluable information for archaeologists and historians. Nowadays, the…
Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming
Giorgio Angelotti, Caroline P. C. Chanel, Adam H. M. Pinto +3
The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time…
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning
Giorgio Angelotti, Nicolas Drougard, Caroline P. C. Chanel
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the d…
Expert-Guided Symmetry Detection in Markov Decision Processes
Giorgio Angelotti, Nicolas Drougard, Caroline P. C. Chanel
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 sample…
An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Proces…
Offline Learning for Planning: A Summary
Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
The training of autonomous agents often requires expensive and unsafe trial-and-error interactions with the environment. Nowadays several data sets containing recorded experiences…
One Class Splitting Criteria for Random Forests
Nicolas Goix, Nicolas Drougard, Romain Brault +1
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 setti…
Qualitative Possibilistic Mixed-Observable MDPs
Nicolas Drougard, Florent Teichteil-Konigsbuch, Jean-Loup Farges +1
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…