Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
This work addresses decision-making under uncertainty for AI and operations research, offering an incremental improvement by adapting existing techniques to a broader class of models.
The paper tackles the problem of solving limited-memory influence diagrams (LIMIDs) by introducing an exact algorithm based on branch-and-bound search, which converts LIMIDs to a smaller decision graph for more efficient search compared to traditional methods.
A limited-memory influence diagram (LIMID) generalizes a traditional influence diagram by relaxing the assumptions of regularity and no-forgetting, allowing a wider range of decision problems to be modeled. Algorithms for solving traditional influence diagrams are not easily generalized to solve LIMIDs, however, and only recently have exact algorithms for solving LIMIDs been developed. In this paper, we introduce an exact algorithm for solving LIMIDs that is based on branch-and-bound search. Our approach is related to the approach of solving an influence diagram by converting it to an equivalent decision tree, with the difference that the LIMID is converted to a much smaller decision graph that can be searched more efficiently.