On the logic of iterated belief revision
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Aggregation and Correlation of Intrusion-Detection Alerts
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A General Model for Epistemic State Revision using Plausibility Measures
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Conditional plausibility measures and Bayesian networks
Journal of Artificial Intelligence Research
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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Ordinal conditional function (OCF) frameworks have been successfully used for modeling belief revision when agents' beliefs are represented in the propositional logic framework. This paper addresses the problem of belief change of graphical representations of uncertain information, called OCF-based networks. In particular, it addresses how to revise OCF-based networks in presence of sequences of observations and interventions. This paper contains three contributions: Firstly, we show that the well-known mutilation and augmentation methods for handling interventions proposed in the framework of probabilistic causal graphs have natural counterparts in OCF causal networks. Secondly, we provide an OCF-based counterpart of an efficient method for handling sequences of interventions and observations by directly performing equivalent transformations on the initial OCF graph. Finally, we highlight the use of OCF-based causal networks on the alert correlation problem in intrusion detection.