This paper surveys methods for representing and reasoning with imperfect information. It opens with an attempt to classify the different types of imperfection that may pervade data, and a discussion of the sources of such imperfections. The classification is then used as a framework for considering work that explicitly concerns the representation of imperfect information, and related work on how imperfect information may be used as a basis for reasoning. The work that is surveyed is drawn from both the field of databases and the field of artificial intelligence. Both of these areas have long been concerned with the problems caused by imperfect information, and this paper stresses the relationships between the approaches developed in each.

We investigate the operator producing a stable theory out of its objective part (A stable theory is a set of beliefs of a rational agent). We characterize the objective parts of stable theories. Finally, we discuss the predicate calculus case. Recent developments in the artificial intelligence and, in particular, strong interest in the formaliza-tions of the common sense reasonings and nonmonotonic logics ([MC], [Re2], [Li], [MDD]) and reasoning about knowledge leads to new interesting developments in the areas of logic previously left almost exclusively to philosophers. These subjects now get attention of computer scientists and mathematicians,