The Transferable Belief Model is a general framework for managing imprecise and uncertain information using belief functions. In this framework, the discounting operation allows to...
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...
Abstract. In this paper, we present a framework for supporting intelligent fault and performance management for communication networks. Belief networks are taken as the basis for k...
Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffrey’s rule, which extends conditioning to uncertain inputs, is used to re...
Salem Benferhat, Didier Dubois, Henri Prade, Mary-...
We consider the problem of updating nonmonotonic knowledge bases represented by epistemic logic programs where disjunctive information and notions of knowledge and beliefs can be ...