[2] viXra:1008.0067 [pdf] submitted on 13 Mar 2010
Authors: Jean Dezert, Florentin Smarandache, Albena Tchamova, Pavlina Konstantinova
Comments: 8 pages
Abstract-In this paper we analyze the performances of a
new probabilistic belief transformation, denoted DSmP, for the
sequential estimation of target ID from classifier outputs in
the Target Type Tracking problem (TTT). We complicate here
a bit the TTT problem by considering three types of targets
(Interceptor, Fighter and Cargo) and show through Monte-Carlo
simulations the advantages of DSmP over the classical pignistic
transformation which is classically used for decision-making
under uncertainty when dealing with belief assignments. Based
on our previous works for the justification of rules of combination
for TTT problem, only the Proportional Conflict Redistribution
rule and the hybrid fusion rules are considered in this work for
their ability to deal consistently with high conflicting sources of
evidence with three different belief assignment modelings.
Category: Artificial Intelligence
[1] viXra:1008.0026 [pdf] replaced on 15 Aug 2010
Authors: Jean Dezert, Florentin Smarandache
Comments: 26 pages
This paper presents the solution about the
threat of a VBIED (Vehicle-Borne Improvised Explosive
Device) obtained with the DSmT (Dezert-Smarandache
Theory). This problem has been proposed recently to the
authors by Simon Maskell and John Lavery as a typical
illustrative example to try to compare the different
approaches for dealing with uncertainty for decision-making
support. The purpose of this paper is to show
in details how a solid justified solution can be obtained
from DSmT approach and its fusion rules thanks to a
proper modeling of the belief functions involved in this
problem.
Category: Artificial Intelligence