Active Information Fusion
Efficient Computation of non-myopic
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.