In this paper, a model is proposed for multi-agent probabilistic reasoning in a distributed environment. Unlike other methods, this model is capable of processing input in a truly...
This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of ...
Tom Kwiatkowksi, Luke S. Zettlemoyer, Sharon Goldw...
In probabilistics, reasoning at optimum entropy (ME-reasoning) has proved to be a most sound and consistent method for inference. This paper investigates its properties in the fram...
We give a quantitative sequential model for noninterference security with probability (but not demonic choice), and a novel refinement order that we prove to be the greatest compo...
Annabelle McIver, Larissa Meinicke, Carroll Morgan
Some errors in our original paper in defining relative reduct with information measures are pointed out in this paper. It is shown that in our original work, Theorems 10 and 19 hol...