DEMPSTER-SHAFER THEORY BY PROBABILISTIC REASONING

Authors

  • Mukherjee C Research Scholar in Computer Science, Magadh University (INDIA)
  • Mukherjee C Department of Mathematics, S.P.S. College, B.R.A.B.University (INDIA)

Keywords:

Probability, Probabilistic Reasoning, Dempster-Shafer theory, Belief function, Plausibility, Mutually exclusive, Lattice

Abstract

Probabilistic reasoning is used when outcomes are unpredictable. We examine the methods which use probabilistic representations for all knowledge and which reason by propagating the uncertainties can arise from evidence and assertions to conclusions. The uncertainties can arise from an inability to predict outcomes due to unreliable, vague, in complete or inconsistent knowledge. Some approaches taken in Artificial Intelligence system to deal with reasoning under similar types of uncertain conditions.

References

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. Grosof, B.N., "An Inequality Paradigm for Probabilistic Knowledge: The Logic of Conditional Probability Intervals," in Uncertainty in Artificial Intelligence, ed. L.N. Kanal and J.F. Lemmer, Amsterdam: Elsevier Science Publishers, 1986, pp.259-275.

. Johnson, R.W., "Independence and Bayesian Updating," in Uncertainty in Artificial Intelligence, ed. L.N. Kanal and J.F. Lemmer, Amsterdam: Elsevier Science Publishers, 1986, pp.197-201.

Lemmer, J .F., "Confidence Factors, Empiricism, and the Dempster-Shafer Theory of Evidence," in Uncertainty in Artificial Intelligence, ed. L.N. Kanal. and J .F. Lemmer, Amsterdam: Elsevier Science Publishers, 1986, pp. 357-369.

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Yen, J ., "A Reasoning Model Based on an Extended Dempster-Shafer Theory," in Proceedings AAAI-86, vol 1., August 1986, pp.125-131.

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Published

2024-02-26

How to Cite

Mukherjee, C., & Mukherjee, G. (2024). DEMPSTER-SHAFER THEORY BY PROBABILISTIC REASONING. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(09), 1083–1086. Retrieved from https://ijact.in/index.php/j/article/view/191

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Section

Original Research Article

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