Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Theory refinement on Bayesian networks
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
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change
Artificial Intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Representing Time in Causal Probabilistic Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Irrelevance in uncertain temporal reasoning
TIME '96 Proceedings of the 3rd Workshop on Temporal Representation and Reasoning (TIME'96)
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Temporal Functional Dependencies and Temporal Nodes Bayesian Networks
The Computer Journal
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty
International Journal of Approximate Reasoning
A framework for reasoning under uncertainty with temporal constraints
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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In this paper, we present a temporal uncertainty-based inferencing paradigm for sensor networks. Multiple sensors observe a phenomenon and then exchange their probability estimates (for the occurrence of an event) with each other. Each node in the network fuses the evidence in such received messages, and computes the probability of occurrence of the relevant event. We develop and apply a temporal relevance decay model that accounts for the possibility that some observations lose their relevance or importance with the passage of time. As an illustrative example, this model is applied to the problems of object detection and tracking using multiple sensors with varying degrees of reliability.