Propositional knowledge base revision and minimal change
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
A Two-Steps Algorithm for Min-Based Possibilistic Causal Networks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Revisions of knowledge systems using epistemic entrenchment
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
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
Interventions in Possibilistic Logic
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
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Belief revision is an important task for designing intelligent systems In the possibility theory framework, considerable work has addressed revising beliefs in a possibilistic logic framework while only few works have addressed a possibilistic revision process in graphical-based frameworks In particular, belief revision of causal product-based possibilistic networks which are the possibilistic counterparts of probabilistic causal networks has not yet been addressed This paper is concerned with revising causal possibilistic networks in presence of two kinds of information: observations and interventions (which are external actions forcing some variables to some specific values) It contains two contributions: we first propose an efficient method for integrating and accepting new observations by directly transforming the initial graph Then we highlight important issues related to belief revision of causal networks with sets of observations and interventions.