A unified setting for inference and decision: An argumentation-based
  approach
  Inferring from inconsistency and making decisions are two problems which have
always been treated separately by researchers in Artificial Intelligence.
Consequently, different models have been proposed for each category. Different
argumentation systems [2, 7, 10, 11] have been developed for handling
inconsistency in knowledge bases. Recently, other argumentation systems [3, 4,
8] have been defined for making decisions under uncertainty. The aim of this
paper is to present a general argumentation framework in which both inferring
from inconsistency and decision making are captured. The proposed framework can
be used for decision under uncertainty, multiple criteria decision, rule-based
decision and finally case-based decision. Moreover, works on classical decision
suppose that the information about environment is coherent, and this no longer
required by this general framework.
