Positive and Negative Explanations of Uncertain Reasoning in the Framework of Possibility Theory
This work addresses the need for improved interpretability in expert systems handling imprecise and uncertain knowledge, but it is incremental as it builds on existing possibility theory frameworks.
The paper tackles the problem of explaining uncertain reasoning in rule-based expert systems by proposing an approach that generates positive and negative explanations for computed possibility distributions, focusing on how distributions are derived and why specific values have particular possibility degrees.
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.