A Value-Driven System for Autonomous Information Gathering
Journal of Intelligent Information Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring an Optimal Amount of Information for Choosing from Alternatives
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Time Sensitive Sequential Myopic Information Gathering
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A statistical decision-making model for choosing among multiple alternatives
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An asymptotically optimal algorithm for the max k-armed bandit problem
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The max K-armed bandit: a new model of exploration applied to search heuristic selection
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Definition and complexity of some basic metareasoning problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
Choosing between heuristics and strategies: an enhanced model for decision-making
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
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This paper proposes a novel technique for allocating information gathering actions in settings where agents need to choose among several alternatives, each of which provides a stochastic outcome to the agent. Samples of these outcomes are available to agents prior to making decisions and obtaining further samples is associated with a cost. The paper formalizes the task of choosing the optimal sequence of information gathering actions in such settings and establishes it to be NP-Hard. It suggests a novel estimation technique for the optimal number of samples to obtain for each of the alternatives. The approach takes into account the trade-offs associated with using prior samples to choose the best alternative and paying to obtain additional samples. This technique is evaluated empirically in several different settings using real data. Results show that our approach was able to significantly outperform alternative algorithms from the literature for allocating information gathering actions in similar types of settings. These results demonstrate the efficacy of our approach as an efficient, tractable technique for deciding how to acquire information when agents make decisions under uncertain conditions.