Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Decomposable negation normal form
Journal of the ACM (JACM)
Background and Perspectives of Possibilistic Graphical Models
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
A survey on knowledge compilation
AI Communications
Logical Compilation of Bayesian Networks with Discrete Variables
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Journal of Artificial Intelligence Research
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
On valued negation normal form formulas
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks using variable elimination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Top-Down Algorithms for Constructing Structured DNNF: Theoretical and Practical Implications
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Experimental comparative study of compilation-based inference in bayesian and possibilitic networks
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Existential closures for knowledge compilation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Toward a more practical unsupervised anomaly detection system
Information Sciences: an International Journal
idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining
Information Sciences: an International Journal
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Probabilistic and possibilistic networks are important tools proposed for an efficient representation and analysis of uncertain information. The inference process has been studied in depth in these graphical models. We cite in particular compilation-based inference which has recently triggered the attention of several researchers. In this paper, we are interested in comparing this inference mechanism in the probabilistic and possibilistic frameworks in order to unveil common points and differences between these two settings. In fact, we will propose a generic framework supporting both Bayesian networks and possibilistic networks (product-based and min-based ones). The proposed comparative study points out that the inference process depends on the specificity of each framework, namely in the interpretation of the handled uncertainty degrees (probability@?possibility) and appropriate operators (^*@?min and +@?max).