Autonomous systems operating in real-world environments must be able to plan, schedule, and execute missions while robustly adapting to uncertainty and disturbances. Previous work...
Julie A. Shah, John Stedl, Brian C. Williams, Paul...
Recent experimental studies have focused on the specialization of different neural structures for different types of instrumental behavior. Recent theoretical work has provided no...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solu...
We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interest...