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 …
Selecting optimal solvers for complex AI tasks grows increasingly difficult as algorithmic options expand. We address this challenge for Stochastic Shortest Path Markov Decision …
Cette thèse présente des contributions en planification automatique sous incertitude, un domaine de l'intelligence artificielle. Ce domaine s'intéresse principalement au calcul de …
Markov Decision Processes (MDPs) are often used in Artificial Intelligence to solve probabilistic sequential decision-making problems. In the last decades, many probabilistic …
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 …
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 …
Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a …
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 …
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 …
Research in automated planning typically focuses on the development of new or improved algorithms. Yet, an equally important but often overlooked topic is that of how to actually …