We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Miss...
Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). ...
Many Ubiquitous computing applications can be considered as planning and acting problems in environments characterised by uncertainty and partial observability. Such systems rely ...
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...