Active Information Fusion



Efficient Computation of non-myopic Value-of-Information

A common scenario in decision making is that, given a decision problem on hand, and a lot of information sources, what information are deserved to be used, or used first? Value-of-information analysis provides a straightforward means for selecting the best next observation to make, for determining whether it is better to gather additional information or to act immediately. However, it requires a consideration of the value of making all possible sequences of observations, which is intractable in real applications. Thus people have to make a myopic assumption: only one additional test will be performed, even when there is an opportunity to make a large number of observations. However, such an assumption is not reasonable in a lot of applications. Thus, we propose an approximate algorithm to compute non-myopic value-of-information efficiently. With this algorithm, the value-of-information can be computed efficiently for any group of information sources, thus makes it possible for people to choose best information sources for decision-making. The figure below illustrates the steps of the proposed algorithm. More detail can be found at Here.






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