Flexible Policy Construction by Information Refinement
  We report on work towards flexible algorithms for solving decision problems
represented as influence diagrams. An algorithm is given to construct a tree
structure for each decision node in an influence diagram. Each tree represents
a decision function and is constructed incrementally. The improvements to the
tree converge to the optimal decision function (neglecting computational costs)
and the asymptotic behaviour is only a constant factor worse than dynamic
programming techniques, counting the number of Bayesian network queries.
Empirical results show how expected utility increases with the size of the tree
and the number of Bayesian net calculations.
