Abstract
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.
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References
I. Jarkass and M. Rombaut. Dealing with uncertainty on the inital state of a petri net. In Fourteenth conference on Uncertainty in Artificial Intelligence, pages 289–295, Madison, Wisconsin, 1998.
M. Courvoisier and R. Valette. Petri nets and artificial intelligence. In International Workshop on Emerging Technologies for Factory Automation, 1992.
T. Murata. Petri nets: properties, analysis and applications. Proceedings of the IEEE, 77(4):541–580, 1989.
M. Rombaut. Prolab2: a driving assistance system. Computer and Mathematics with Applications, 22:103–118, 1995.
D. Dubois and H. Prade. Possibility Theory: An approach to computerized processing of uncertainty. Plenum Press, New-York, 1988.
J. Cardoso and H. Camargo. Fuzziness in Petri nets. Physica-Verlag, Heidelberg, 1998.
P. Smets and R. Kennes. The Transferable Belief Model. Artificial Intelligence,66:191–243, 1994.
G. J. Klir. Measures of uncertainty in the Dempster-Shafer theory of evidence. In R. R. Yager, M. Fedrizzi, and J. Kacprzyk, editors, Advances in the Dempster-Shafer theory of evidence, pages 35–49. John Wiley and Sons, New-York, 1994.
P. Smets. Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning, 9:1–35, 1993.
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© 1999 Springer-Verlag Berlin Heidelberg
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Rombaut, M., Jarkass, I., Denœux, T. (1999). State Recognition in Discrete Dynamical Systems using Petri Nets and Evidence Theory. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_32
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DOI: https://doi.org/10.1007/3-540-48747-6_32
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