On the logic of iterated belief revision
Artificial Intelligence
Probability updating using second order probabilities and conditional event algebra
Information Sciences: an International Journal
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Postulates for Conditional Belief Revision
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Focusing vs. Belief Revision: A Fundamental Distinction When Dealing with Generic Knowledge
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Belief revision and information fusion on optimum entropy: Research Articles
International Journal of Intelligent Systems - Uncertain Reasoning (Part 2)
Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Probabilistic belief change: expansion, conditioning and constraining
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Updating sets of probabilities
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probability update: conditioning vs. cross-entropy
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Forgetting and knowledge update
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
In this paper, we present a revision strategy of revising a conditional probabilistic logic program (PLP) when new information is received (which is in the form of probabilistic formulae), through the technique of variable forgetting. We first extend the traditional forgetting method to forget a conditional event in PLPs. We then propose two revision operators to revise a PLP based on our forgetting method. By revision through forgetting, the irrelevant knowledge in the original PLP is retained according to the minimal change principle. We prove that our revision operators satisfy most of the postulates for probabilistic belief revision. A main advantage of our revision operators is that a new PLP is explicitly obtained after revision, since our revision operator performs forgetting a conditional event at the syntax level.