Strategy Selection in Influence Diagrams using Imprecise Probabilities
  This paper describes a new algorithm to solve the decision making problem in
Influence Diagrams based on algorithms for credal networks. Decision nodes are
associated to imprecise probability distributions and a reformulation is
introduced that finds the global maximum strategy with respect to the expected
utility. We work with Limited Memory Influence Diagrams, which generalize most
Influence Diagram proposals and handle simultaneous decisions. Besides the
global optimum method, we explore an anytime approximate solution with a
guaranteed maximum error and show that imprecise probabilities are handled in a
straightforward way. Complexity issues and experiments with random diagrams and
an effects-based military planning problem are discussed.
