Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Problem formulation as the reduction of a decision model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Acquiring an Optimal Amount of Information for Choosing from Alternatives
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Noisy information value in utility-based decision making
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Preference elicitation for interface optimization
Proceedings of the 18th annual ACM symposium on User interface software and technology
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
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
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Optimal testing of structured knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient sensor selection for active information fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
A utility-theoretic approach to privacy in online services
Journal of Artificial Intelligence Research
The impact of available information on negotiation results
Annals of Mathematics and Artificial Intelligence
Learning complex concepts using crowdsourcing: a Bayesian approach
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Journal of Artificial Intelligence Research
Light at the end of the tunnel: a Monte Carlo approach to computing value of information
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Efficiently gathering information in costly domains
Decision Support Systems
An exact algorithm for computing the same-decision probability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Look versus leap: computing value of information with high-dimensional streaming evidence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.14 |
It is argued that decision analysts and expert-system designers have avoided the intractability of exact computation of the value of information by relying on a myopic assumption that only one additional test will be performed, even when there is an opportunity to make large number of observations. An alternative to the myopic analysis is presented. In particular, an approximate method for computing the value of information of a set of tests, which exploits the statistical properties of large samples, is given. The approximation is linear in the number of tests, in contrast with the exact computation, which is exponential in the number of tests. The approach is not as general as in a complete nonmyopic analysis, in which all possible sequences of observations are considered. In addition, the approximation is limited to specific classes of dependencies among evidence and to binary hypothesis and decision variables. Nonetheless, as demonstrated with a simple application, the approach can offer an improvement over the myopic analysis.