Belief Base Revision for Further Improvement of Unified Answer Set Programming
This work addresses belief base revision for AI systems dealing with non-monotonic reasoning, but it appears incremental as it builds on existing Unified Answer Set Programming and revision strategies.
The authors tackled the problem of revising belief bases represented by Unified Answer Set Programs, which handle imprecise and uncertain information, by developing a revision operator using the Removed Set Revision strategy and characterizing it with respect to postulates.
A belief base revision is developed. The belief base is represented using Unified Answer Set Programs which is capable of representing imprecise and uncertain information and perform nonomonotonic reasoning with them. The base revision operator is developed using Removed Set Revision strategy. The operator is characterized with respect to the postulates for base revisions operator satisfies.