Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Advances in probabilistic reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Planning and control
Characterizing diagnoses and systems
Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Safeware: system safety and computers
Safeware: system safety and computers
Bayesian Networks for Reliability Analysis of Complex Systems
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
A Comparative Analysis of Horn Models and Bayesian Networks for Diagnosis
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
Hybrid processing of beliefs and constraints
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Parametric dependability analysis through probabilistic Horn abduction
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to their use in dependability analysis. The aim of this paper is to propose BN as a suitable tool for dependability analysis, by challenging the formalisnl with basic issues arising in dependability tasks. We will discuss how both modeling and analysis issues can be naturally dealt with by BN. Moreover, we will show how some limitations intrinsic to combinatorial dependability methods such as Fault Trees can be overcome using BN. This will be pursued through the study of a real-world example concerning the reliability analysis of a redundant digital Programmable Logic Controller (PLC) with majority voting 2:3