Plausibility Measures: A User's Guide
  We examine a new approach to modeling uncertainty based on plausibility
measures, 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 in question, 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.
