A New Approach for Revising Logic Programs
It addresses an under-explored problem in AI for knowledge representation, focusing on non-monotonic logics, but appears incremental as it adapts revision concepts to a specific logic framework.
The paper tackles belief revision for non-monotonic logic programs under answer set semantics, proposing a syntactic revision function and proving a representation theorem for it.
Belief revision has been studied mainly with respect to background logics that are monotonic in character. In this paper we study belief revision when the underlying logic is non-monotonic instead--an inherently interesting problem that is under explored. In particular, we will focus on the revision of a body of beliefs that is represented as a logic program under the answer set semantics, while the new information is also similarly represented as a logic program. Our approach is driven by the observation that unlike in a monotonic setting where, when necessary, consistency in a revised body of beliefs is maintained by jettisoning some old beliefs, in a non-monotonic setting consistency can be restored by adding new beliefs as well. We will define a syntactic revision function and subsequently provide representation theorem for characterising it.