Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Improved Algorithms for Linear Inequalities with Two Variables per Inequality
SIAM Journal on Computing
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Introduction to Algorithms
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evidence Propagation and Value of Evidence on Influence Diagrams
Operations Research
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Decision Analysis
Efficient active fusion for decision-making via VOI approximation
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A decision theoretic model for stress recognition and user assistance
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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
Using potential influence diagrams for probabilistic inference and decision making
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Influence diagrams with multiple objectives and tradeoff analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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
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 | 0.00 |
In an influence diagram (ID), value-of-information (VOI) is defined as the difference between the maximum expected utilities with and without knowing the outcome of an uncertainty variable prior to making a decision. It is widely used as a sensitivity analysis technique to rate the usefulness of various information sources, and to decide whether pieces of evidence are worth acquisition before actually using them. However, due to the exponential time complexity of exactly computing VOI of multiple information sources, decision analysts and expert-system designers focus on the myopic VOI, which assumes observing only one information source, even though several information sources are available. In this paper, we present an approximate algorithm to compute non-myopic VOI efficiently by utilizing the central-limit theorem. The proposed method overcomes several limitations in the existing work. In addition, a partitioning procedure based on the d-separation concept is proposed to further improve the computational complexity of the proposed algorithm. Both the experiments with synthetic data and the experiments with real data from a real-world application demonstrate that the proposed algorithm can approximate the true non-myopic VOI well even with a small number of observations. The accuracy and efficiency of the algorithm makes it feasible in various applications where efficiently evaluating a large amount of information sources is necessary.