Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Greedy algorithms for sequential sensing decisions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Journal of Artificial Intelligence Research
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth 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
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Combining human and machine intelligence in large-scale crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Lifelong learning for acquiring the wisdom of the crowd
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Calculating the expected value of information (VOI) for sequences of observations under uncertainty is intractable, as branching trees of potential outcomes of sets of observations must be considered in the general case. We address the combinatorial challenge of computing ideal observational policies in situations where long sequences of weak evidential updates may have to be considered. We introduce and validate the use of Monte Carlo procedures for computing VOI with such long evidential sequences. We evaluate the procedure on a synthetic dataset and on a challenging citizen-science problem and demonstrate how it can effectively cut through the intractability of the combinatorial space.