Research over the past several decades in learning logical and probabilistic models has greatly increased the range of phenomena that machine learning can address. Recent work has ...
Most financial time series processes are nonstationary and their frequency characteristics are time-dependant. In this paper we present a time series summarization and prediction ...
We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for...
Brandon Bennett, Derek R. Magee, Anthony G. Cohn, ...
Answer Set Programming (ASP) and Constraint Logic Programming over finite domains (CLP(FD)) are two declarative programming paradigms that have been extensively used to encode ap...
Agostino Dovier, Andrea Formisano, Enrico Pontelli
POMDPs are a popular framework for representing decision making problems that contain uncertainty. The high computational complexity of finding exact solutions to POMDPs has spaw...