The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Inference in directed evidential networks based on the transferable belief model
International Journal of Approximate Reasoning
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
Evidential reasoning with conditional belief functions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Representing belief function knowledge with graphical models
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
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Eliciting the cause of an event will be easier if an agent can directly intervene on some variables by forcing them to take a specific value. The state of the target variable is therefore totally dependent of this external action and independent of its original causes. However in real world applications, performing such perfect interventions is not always feasible. In fact, an intervention can be uncertain in the sense that it may uncertainly occur. It can also have uncertain consequences which means that it may not succeed to put its target into one specific value. In this paper, we use the belief function theory to handle uncertain interventions that could have uncertain consequences. Augmented causal belief networks are used to model uncertain interventions.