Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making proble...
It has been shown recently that deterministic conformant planning problems can be translated into classical problems that can be solved by off-the-shelf classical planners. In this...
Motion planning for mobile agents, such as robots, acting in the physical world is a challenging task, which traditionally concerns safe obstacle avoidance. We are interested in p...
Planning for partially observable, nondeterministic domains is a very signi cant and computationally hard problem. Often, reasonable assumptions can be drawn over expected/nominal...
Controller synthesis consists in automatically building controllers taking as inputs observation data and returning outputs guaranteeing that the controlled system satisfies some d...