This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these app...
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
The majority of real-world probabilistic systems are used by more than one user, thus a utility model must be elicited separately for each newuser. Utility elicitation is long and...
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuv...
People using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals ...