Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
“Conditional inter-causally independent” node distributions, a property of “noisy-or” models
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Learning Bayesian Networks
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty
International Journal of Approximate Reasoning
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Real-time inference with large-scale temporal bayes nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic temporal reasoning with endogenous change
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
TEMPER: a temporal programmer for time-sensitive control of discrete event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Unsupervised plan detection with factor graphs
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
A task-resource allocation method based on effectiveness
Knowledge-Based Systems
International Journal of Approximate Reasoning
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This paper enhances the Timed Influence Nets (TIN) based formalism to model uncertainty in dynamic situations. The enhancements enable a system modeler to specify persistence and time-varying influences in a dynamic situation that the existing TIN fails to capture. The new class of models is named Dynamic Influence Nets (DIN). Both TIN and DIN provide an alternative easy-to-read and compact representation to several time-based probabilistic reasoning paradigms including Dynamic Bayesian Networks. The Influence Net (IN) based approach has its origin in the Discrete Event Systems modeling. The time delays on arcs and nodes represent the communication and processing delays, respectively, while the changes in the probability of an event at different time instants capture the uncertainty associated with the occurrence of the event over a period of time.