Propositional knowledge base revision and minimal change
Artificial Intelligence
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Logic programming with ordered disjunction
Eighteenth national conference on Artificial intelligence
An abductive framework for computing knowledge base updates
Theory and Practice of Logic Programming
On properties of update sequences based on causal rejection
Theory and Practice of Logic Programming
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Update Sequences in Generalised Answer Set Programming Based on Structural Properties
MICAI '06 Proceedings of the Fifth Mexican International Conference on Artificial Intelligence
A unified framework for representing logic program updates
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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Update of knowledge bases is becoming an important topic in Artificial Intelligence and a key problem in knowledge representation and reasoning. One of the latest ideas to update logic programs is choosing between models of Minimal Generalised Answer Sets to overcome disadvantages of previous approaches. This paper describes an implementation of the declarative version of updates sequences that has been proposed as an alternative to syntax-based semantics. One of the main contributions of this implementation is to use DLV's Weak Constraints to compute the model(s) of an update sequence, besides presenting the precise definitions proposed by the authors and an online solver. As a result, the paper makes an outline of the basic structure of the system, describes the employed technology, discusses the major process of computing the models, and illustrates the system through examples.