A Study of Kernel Contraction in EL

An intelligent agent must be able to change its beliefs in a rational manner and with a plausible outcome. While the central belief change theories have well-developed formal characterisations, there has been limited work on applying these theories in a practical setting. In this paper we examine belief base contraction, specifically kernel contraction, in the framework of the description logic EL. This is potentially interesting first, because EL is used in large-scale knowledge bases and second, because the EL constructs for structuring knowledge allow the specification of heuristics to choose an intuitive, commonsense result for the contraction. We introduce algorithms that perform belief contraction based on hitting sets, and examine different ways of finding a suitable hitting set. Heuristics based on localization and specificity are introduced. As well, our main contraction operators are shown to conform to Hansson’s postulates for smooth base contraction.