Uncertainty in Artificial Intelligence
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Nilsson's Probabilistic Entailment Extended to Dempster-Shafer Theory
Mary McLeish
Probabilistic logic has been discussed in a recent paper by N. Nilsson [12]. An entailment scheme is proposed which can predict the probability of an event when the probabilities of certain other connected events are known. This scheme involves the use of a maximum entropy method proposed by P. Cheeseman in [3]. The model uses vectors which represent certain possible states of the world. Only consistent such vectors are entered into the probability scheme. As a result, entailment does not always yield an acceptable result and cannot be applied to real situations which could arise. This paper investigates a technique to overcome this problem, which involves extending the idea of probabilistic logic and the maximum entropy approach to Dempster-Shafer theory. A new entailment scheme for belief functions is used which produces well-defined results, even when only "consistent" worlds are being considered. The paper also reconsiders an earlier attempt by the author [6,7] to model default reasoning (and subsequent nonmonotonicity) by adding inconsistent vectors to Nilsson's model. In the extended setting, more sensible entailment values are obtained than in the previous work.
Keywords: Probabilistic Logic, Maximum Entropy Method, Dempster-Shafer theory
Pages: 23-34
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PDF Link: /papers/87/p23-mcleish.pdf
AUTHOR = "Mary McLeish ",
TITLE = "Nilsson's Probabilistic Entailment Extended to Dempster-Shafer Theory",
BOOKTITLE = "Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1987",
PAGES = "23--34"

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