An Anytime Algorithm for Decision Making under Uncertainty
  We present an anytime algorithm which computes policies for decision problems
represented as multi-stage influence diagrams. Our algorithm constructs
policies incrementally, starting from a policy which makes no use of the
available information. The incremental process constructs policies which
includes more of the information available to the decision maker at each step.
While the process converges to the optimal policy, our approach is designed for
situations in which computing the optimal policy is infeasible. We provide
examples of the process on several large decision problems, showing that, for
these examples, the process constructs valuable (but sub-optimal) policies
before the optimal policy would be available by traditional methods.
