KAKENHI(S) Discrete Structure Manipulation System Project (Hokkaido Univ.)

Graphical models provide a powerful framework for reasoning with probabilistic information. Combinatorial maximization, or maximum a posteriori (MAP) tasks arise in many applications and often can be efficiently solved by search schemes, especially in the context of AND/OR search spaces that are sensitive to the underlying problem structure.
In this talk, I present the power of limited memory best-first search over AND/OR search spaces, named RBFAOO, which performs exact MAP inference over graphical models. I also present parallelized versions of RBFAOO which run in both shared-memory and distributed-memory environments. I show that RBFAOO is empirically superior to the current state-of-the-art approaches based on AND/OR search, especially on very hard problem instances.