Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A Comparative Study of Six Formal Models of Causal Ascription
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Causality and Dynamics of Beliefs in Qualitative Uncertainty Frameworks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Quantitative Possibilistic Networks: Handling Interventions and Ascribing Causality
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Interventions in Possibilistic Logic
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
Compiling min-based possibilistic causal networks: a mutilated-based approach
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Jeffrey's rule of conditioning in a possibilistic framework
Annals of Mathematics and Artificial Intelligence
Belief revision of product-based causal possibilistic networks
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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This paper contains two important contributions for the development of possibilistic causal networks. The first one concerns the representation of interventions in possibilistic networks. We provide the counterpart of the "DO" operator, recently introduced by Pearl, in possibility theory framework. We then show that interventions can equivalently be represented in different ways in possibilistic causal networks. The second main contribution is a new propagation algorithm for dealing with both observations and interventions. We show that our algorithm only needs a small extra cost for handling interventions and is more appropriate for handling sequences of observations and interventions.