Converting an Integer to a Decimal String in Under Two Nanoseconds
Converting binary integers to variable-length decimal strings is a fundamental operation in computing. Conventional fast approaches rely on recursive division and small lookup …
Jaël Champagne Gareau
Chercheur postdoctoral en informatique
Je suis actuellement chercheur postdoctoral en informatique à l’Université
TÉLUQ, où mes travaux portent sur l’accélération de la conversion de nombres
entiers et flottants en chaînes de caractères décimales. Au cours de mon
doctorat, j’ai conçu des algorithmes et des structures de données exploitant
l’architecture moderne des ordinateurs afin de résoudre de grandes instances
de processus décisionnels de Markov (MDP). Durant ma maîtrise, j’ai développé
des algorithmes de planification d’itinéraires pour véhicules électriques,
visant à déterminer le chemin optimal entre deux points tout en minimisant
le temps total du trajet (déplacement, recharge et attente aux bornes).
Converting Binary Floating-Point Numbers to Shortest Decimal Strings: An Experimental Review
When sharing or logging numerical data, we must convert binary floating-point numbers into their decimal string representations. For example, the number $\pi$ might become …
Topology-Driven Solver Selection for Stochastic Shortest Path MDPs via Explainable Machine Learning
Selecting optimal solvers for complex AI tasks grows increasingly difficult as algorithmic options expand. We address this challenge for Stochastic Shortest Path Markov Decision …
Mathieu Gravel
Résolution efficace de processus décisionnels de Markov par l'exploitation d'approches structurelles et algorithmiques tirant parti de l'architecture moderne des ordinateurs
Cette thèse présente des contributions en planification automatique sous incertitude, un domaine de l'intelligence artificielle. Ce domaine s'intéresse principalement au calcul de …
Towards Topologically Diverse Probabilistic Planning Benchmarks
Markov Decision Processes (MDPs) are often used in Artificial Intelligence to solve probabilistic sequential decision-making problems. In the last decades, many probabilistic …
Increased Plan Stability in Cooperative Electric Vehicles Path-Planning
The Cooperative Electric Vehicles Planning Problem (CEVPP) has recently been proposed as a multi-agent variant of the Electric Vehicle Path-Planning Problem (EVPP). It consists in …
Cooperative Electric Vehicles Planning
This paper introduces the Cooperative Electric Vehicles Planning Problem (CEVPP), which consists in finding a path for each vehicle of a fleet of electric vehicles, such that the …
Cache-Efficient Dynamic Programming MDP Solver
Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a …
Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function
This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given …
pcTVI: Parallel MDP Solver Using a Decomposition Into Independent Chains
Markov Decision Processes (MDPs) are useful to solve real-world probabilistic planning problems. However, finding an optimal solution in an MDP can take an unreasonable amount of …