In many sensing applications we must continuously gather information to provide a good estimate of the state of the environment at every point in time. A robot may tour an environ...
Alexandra Meliou, Andreas Krause, Carlos Guestrin,...
Partially Observable Markov Decision Processes (POMDPs) provide an appropriately rich model for agents operating under partial knowledge of the environment. Since finding an opti...
Yan Virin, Guy Shani, Solomon Eyal Shimony, Ronen ...
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diag...
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typ...
In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of serv...
W. T. Luke Teacy, Georgios Chalkiadakis, Alex Roge...