Plausibility Measures: A User's Guide
N. Friedman and J. Y. Halpern
In Proc. Eleventh Conf. on
Uncertainty in Artificial Intelligence (UAI 95).
a new approach to modeling uncertainty based on
where a plausibility measure just associates with an event its
plausibility, an element is some partially ordered set.
This approach is easily seen to generalize
other approaches to modeling uncertainty, such as probability measures,
belief functions, and possibility measures.
The lack of structure in a plausibility measure makes it easy for
us to add structure on an ``as needed'' basis, letting us examine
what is required to ensure that a plausibility measure has certain
properties of interest.
This gives us insight into the essential features of the properties
while allowing us to prove general results that apply to
many approaches to reasoning about uncertainty.
Plausibility measures have already proved useful in analyzing
default reasoning. In this paper, we examine their "algebraic
properties", analogues to the use of + and * in probability
theory. An understanding of such properties will be essential if
plausibility measures are to be used in practice
as a representation tool.
Back to Nir's