We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previously-bui...
Benjamin Stewart, Jonathan Ko, Dieter Fox, Kurt Ko...
This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling ...
We address the problem of Bayesian estimation where the statistical relation between the signal and measurements is only partially known. We propose modeling partial Baysian knowl...
Website capacity determination is crucial to measurement-based access control, because it determines when to turn away excessive client requests to guarantee consistent service qu...
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...