Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Maintaining knowledge about temporal intervals
Communications of the ACM
A logic and time nets for probabilistic inference
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Probabilistic temporal reasoning with endogenous change
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Time-critical action: representations and application
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Temporal Bayesian Network of Events for Diagnosis and Prediction in Dynamic Domains
Applied Intelligence
COMPARISON OF TWO TYPES OF EVENT BAYESIAN NETWORKS: A CASE STUDY
Applied Artificial Intelligence
Temporal Bayesian networks for scenario recognition
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Learning temporal Bayesian networks for power plant diagnosis
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Artificial Intelligence in Medicine
Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks
Artificial Intelligence in Medicine
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
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Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relation. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and event prediction with good results.