Automatic differentiation of non-holonomic fast marching for computing most threatening trajectories under sensors surveillance

We consider a two player game, where a first player has to install a surveillance system within an admissible region. The second player needs to enter the the monitored area, visit a target region, and then leave the area, while minimizing his overall probability of detection. Both players know the target region, and the second player knows the surveillance installation details.Optimal trajectories for the second player are computed using a recently developed variant of the fast marching algorithm, which takes into account curvature constraints modeling the second player vehicle maneuverability. The surveillance system optimization leverages a reverse-mode semi-automatic differentiation procedure, estimating the gradient of the value function related to the sensor location in time N log N

Stochastic Metaheuristics as Sampling Techniques using Swarm Intelligence

Optimization problems appear in many fields, as various as identification problems, supervised learning of neural networks, shortest path problems, etc. Metaheuristics [22] are a family of optimization algorithms, often applied to "hard " combinatorial problems for which no more efficient method is known. They have the advantage of being generi

Generalized fast marching method for computing highest threatening trajectories with curvature constraints and detection ambiguities in distance and radial speed

Work presented at the 9th Conference on Curves and Surfaces, 2018, ArcachonWe present a recent numerical method devoted to computing curves that globally minimize an energy featuring both a data driven term, and a second order curvature penalizing term. Applications to image segmentation are discussed. We then describe in detail recent progress on radar network configuration, in which the optimal curves represent an opponent's trajectories

Randomized Optimization Framework tailored for Benchmarking & Auto-Design: Paradiseo

International audienceParadiseo is an open-source full-featured evolutionary computation framework which main purpose is to help you write your own stochastic optimization algorithms, insanely fast: Choose an algorithm template, select operators of interest, plug a benchmark and let it design the algorithm for you!It focus on the efficiency of the implementation of solvers, by providing: - a modular design for several types of paradigms,- the largest codebase of existing components,- tools for automated design and selection of algorithms,- a focus on speed and several parallelization options

Feedback on the use of PDDL solvers in an industrial R&D context

DoctoralFollowing its initial investment on automated planning solvers with INRIA and ONERA, which led to the win of the IPC 2011 temporal satisficing track, several use cases have been solved thanks to this technology. However, the use of PDDL solvers has been put on hold after this period, mainly because of scalability issues. This presentation will try to explain the how and why of this decision, hoping that the academic community will be able to solve the underlying problems

Quantile-like measures on multi-dimensional distributions of closed sets: Application in stochastic optimization

DoctoralThe Empirical Attainment Function is a 2D empirical distribution function, which is actually a distribution of convergence trajectories.It can be efficiently computed, using the monotonic nature of those trajectories.We argue that it extends both the classical fixed-target and fixed-budget ECDF, which are the current gold-standardto assess black-box heuristic search performances. We argue that, in addition to capturing more information,if is also less prone to approximation error, as it does not need a pre-defined target set

De la planification centralisée à l’intelligence en essaim avec des drones

International audienceDEU

Practical Tools for Improving Reproducibility (of Bioinformatics)

DoctoralExperimental studies are prevalent in (Bio)informatics, as in many fields involving computer science. While reproducibility is crucial for science, it is difficult and often overlooked. This presentation introduces a curated list of tools that can help improving reproducibility for experimental computer science. It may incidentally insists on testing..

Extensible Logging and Empirical Attainment Function for IOHexperimenter

11 pagesIn order to allow for large-scale, landscape-aware, per-instance algorithm selection, a benchmarking platform software is key. IOHexperimenter provides a large set of synthetic problems, a logging system, and a fast implementation. In this work, we refactor IOHexperimenter's logging system, in order to make it more extensible and modular. Using this new system, we implement a new logger, which aims at computing performance metrics of an algorithm across a benchmark. The logger computes the most generic view on an anytime stochastic heuristic performances, in the form of the Empirical Attainment Function (EAF). We also provide some common statistics on the EAF and its discrete counterpart, the Empirical Attainment Histogram. Our work has eventually been merged in the IOHexperimenter codebase