Abstract
We present a new methodology for decision-making support based on belief functions thanks to a new theoretical canonical decomposition of dichotomous basic belief assignments (BBAs) that has been developed recently. This decomposition based on proportional conflict redistribution rule no 5 (PCR5) always exists and is unique. This new PCR5-based decomposition method circumvents the exponential complexity of the direct fusion of BBAs with PCR5 rule and it allows to fuse quickly many sources of evidences. The method we propose in this paper provides both a decision and an estimation of the quality of the decision made, which is appealing for decision-making support systems.
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Notes
- 1.
I.e. the solution, or the decision to take.
- 2.
This is so-called Shafer’s model of FoD [5].
- 3.
The complete ignorance is denoted \(\varTheta \) in Shafer’s book [1].
- 4.
\(Disj(m_p,m_c)\) denotes symbolically the disjunctive fusion of \(m_p\) with \(m_c\).
- 5.
Taking \(y=1\) would means that \(x(1-y)=0\) but \(m(A)=a\) with \(a\ne 0\) in general, so the choice of \(y=1\) is not possible.
- 6.
- 7.
- 8.
Because there is no simple analytical expressions for solutions x and y of PCR5-based canonical decomposition.
- 9.
The solutions can be easily obtained with the roots command of Matlabâ„¢.
- 10.
This point is not detailed here because is out of the scope of this paper.
- 11.
Where the complexity is linear with the number of dichotomous BBAs to fuse.
- 12.
For clarity, we need to introduce in the notations a superscript to indicate the FoD we are working on.
References
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Zadeh, L.A.: On the validity of Dempster’s rule of combination. ERL Memo M79/24, Department of EECS, University of California, Berkeley, U.S.A. (1979)
Zadeh, L.A.: A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. Al Mag. 7(2), 85–90 (1986)
Dezert, J., Tchamova, A.: On the validity of Dempster’s fusion rule and its interpretation as a generalization of Bayesian fusion rule. Int. J. Intell. Syst. 29(3), 223–252 (2014)
Smarandache, F., Dezert J. (eds.): Advances and Applications of DSmT for Information Fusion, vols. 1–4. American Research Press, Rehoboth (2004–2015)
Dezert, J., Smarandache, F.: Canonical decomposition of dichotomous basic belief assignment. Int. J. Intell. Syst. 35(7), 1105–1125 (2020)
Smets, P.: The canonical decomposition of a weighted belief. In: Proceedings of International Joint Conference on Artificial Intelligence, San Mateo, CA, USA, pp. 1896–1901 (1995)
Yager, R.: On the Dempster-Shafer framework and new combination rules. Inf. Sci. 41, 93–138 (1987)
Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Comput. Intell. 4, 244–264 (1988)
Dezert, J., Smarandache, F., Tchamova, A., Han, D.: Fast fusion of basic belief assignments defined on a dichotomous frame of discernment. In: Proceedings of Fusion 2020, Pretoria, South Africa (2020)
Dezert, J., Fidanova, S., Tchamova, A.: Fast BF-ICrA method for the evaluation of MO-ACO algorithm for WSN layout. In: Proceedings of FedCSIS International Conference, Sofia, Bulgaria (2020)
Han, D., Dezert, J., Yang, Y.: Belief interval based distances measures in the theory of belief functions. IEEE Trans. SMC 486, 833–850 (2018)
Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994)
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Dezert, J., Smarandache, F. (2021). Canonical Decomposition of Basic Belief Assignment for Decision-Making Support. In: Simian, D., Stoica, L.F. (eds) Modelling and Development of Intelligent Systems. MDIS 2020. Communications in Computer and Information Science, vol 1341. Springer, Cham. https://doi.org/10.1007/978-3-030-68527-0_7
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