One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some a...
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dim...
Constraint Logic Programming (CLP) and Abductive Logic Programming (ALP) share the important concept of conditional answer. We exploit their deep similarities to implement an effic...
Marco Gavanelli, Evelina Lamma, Paola Mello, Miche...