Positive and Negative Explanations of Uncertain Reasoning in the
  Framework of Possibility Theory
  This paper presents an approach for developing the explanation capabilities
of rule-based expert systems managing imprecise and uncertain knowledge. The
treatment of uncertainty takes place in the framework of possibility theory
where the available information concerning the value of a logical or numerical
variable is represented by a possibility distribution which restricts its more
or less possible values. We first discuss different kinds of queries asking for
explanations before focusing on the two following types : i) how, a particular
possibility distribution is obtained (emphasizing the main reasons only) ; ii)
why in a computed possibility distribution, a particular value has received a
possibility degree which is so high, so low or so contrary to the expectation.
The approach is based on the exploitation of equations in max-min algebra. This
formalism includes the limit case of certain and precise information.
