Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
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 Factor Tree Inference Algorithm for Bayesian Networks and Its Application
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Efficient active fusion for decision-making via VOI approximation
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Sensor selection for active information fusion
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A factorization approach to evaluating simultaneous influence diagrams
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Situation-specific intention recognition for human-robot cooperation
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Modelling stress recognition in conflict resolution scenarios
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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In our previous paper, we formalized an active information fusion framework based on dynamic Bayesian networks to provide active information fusion. This paper focuses on a central issue of active information fusion, i.e., the efficient identification of a subset of sensors that are most decision relevant and cost effective. Determining the most informative and cost-effective sensors requires an evaluation of all the possible subsets of sensors, which is computationally intractable, particularly when information-theoretic criterion such as mutual information is used. To overcome this challenge, we propose a new quantitative measure for sensor synergy based on which a sensor synergy graph is constructed. Using the sensor synergy graph, we first introduce an alternative measure to multisensor mutual information for characterizing the sensor information gain. We then propose an approximated nonmyopic sensor selection method that can efficiently and near-optimally select a subset of sensors for active fusion. The simulation study demonstrates both the performance and the efficiency of the proposed sensor selection method.