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 …
Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a …
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 …
This project aimed at finding different ways to improve (SSP-)MDP planners performance when considering computer architectures (e.g., cache-memory, parallelism)
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 …