Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
— The problem of simultaneous localization and mapping (SLAM) is one of the most studied in the robotics literature. Most existing approaches, however, focus on scenarios where l...
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
A Web proxy must accurately predict network performance between itself and its servers and clients in order to make good distillation decisions. In this paper, we show that the cu...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...