Artificial Intelligence
Measures of uncertainty in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Dealing with uncertainty on the initial state of a petri net
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
ProLab2: A driving assistance system
Mathematical and Computer Modelling: An International Journal
International Journal of Approximate Reasoning
Shape-Motion based athlete tracking for multilevel action recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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A method is proposed for determining the state of a dynamical system modeled by a Petri net, using observations of its inputs. The initial state of the system may be totally or partially unknown, and sensor reports may be uncertain. In previous work, a belief Petri net model using the formalism of evidence theory was defined, and the resolution of the system was done heuristically by adapting the classical evolution equations of Petri nets. In this paper, a more principled approach based on the Transferable Belief Model is adopted, leading to simpler computations. An example taken from an intelligent vehicle application illustrates the method throughout the paper.