Selecting optimal solvers for complex AI tasks grows increasingly difficult as algorithmic options expand. We address this challenge for Stochastic Shortest Path Markov Decision …
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 …
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 …
This paper introduces a new algorithm for solving the discrete grid-based coverage path-planning (CPP) problem. This problem consists in finding a path that covers a given region …
This paper presents a novel approach for trip reconstruction and transport mode detection. While traditional methods use a fixed GPS sampling rate, our proposed method uses a …
In the last few years, several studies have considered different variants of the Electric Vehicle Journey Planning (EVJP) problem that consists in finding the shortest path …