Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Active fusion—a new method applied to remote sensing image interpretation
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
A decision theoretic model for stress recognition and user assistance
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Efficient value of information computation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Efficient sensor selection for active information fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
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Active fusion is a process that purposively selects the most informative information from multiple sources as well as combines these information for achieving a reliable result efficiently. This paper presents a general mathematical framework based on Influence Diagrams (IDs) for active fusion and timely decision making. Within this framework, an approximation algorithm is proposed to efficiently compute nonmyopic value-of-information (VOI) for multiple sensory actions. Meanwhile a sensor selection algorithm is proposed to choose optimal sensory action sets efficiently. Both the experiments with synthetic data and real data from a real-world application demonstrate that the proposed framework together with the algorithms are well suited to applications where the decision must be made efficiently and timely from dynamically available information of diverse and disparate sources.