Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search

A limited-memory influence diagram (LIMID) is a generalization of a traditional influence diagram in which the assumptions of regularity and no-forgetting are relaxed. Lauritzen and Nilsson (2001) introduced this model and proposed an iterative algorithm, called single policy updating (SPU), that converges to a locally optimal solution. In this paper, we describe a branch-andbound algorithm that finds a globally optimal solution. Initial experiments suggest that the branch-and-bound approach scales better than SPU.