A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Maintaining knowledge about temporal intervals
Communications of the ACM
Diagnosis of discrete-event systems from uncertain temporal observations
Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A temporal Bayesian network for diagnosis and prediction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
Integrating rush orders into existent schedules for a complex job shop problem
Applied Intelligence
Detecting marginal and conditional independencies between events and learning their causal structure
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
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In some domains like industry, medicine, communications, speech recognition, planning, tutoring systems, and forecasting; the timing of observations (symptoms, measures, test, events, as well as faults) play a major role in diagnosis and prediction. This paper introduces a new formalism to deal with uncertainty and time using Bayesian networks called Temporal Bayesian Network of Events (TBNE). In a TBNE each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relationship. A temporal node represents the time that a variable changes state, including an option of no-change. 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 subsystem of a thermal power plant, in which this approach is used for fault diagnosis and event prediction with good results. The TBNE model can be used for the diagnosis of a cascade of anomalies arising with certain delays; this situation is typical in the diagnosis of medical and industrial processes.