Graphical models, including constraint networks, CNF formulas, Bayesian networks, Markov random fields and influence diagrams, are knowledge representation schemes that capture independencies in the knowledge base and support efficient, graph-based algorithms for a variety of reasoning tasks, including scheduling, planning, diagnosis and situation assessment, design, and hardware and software verification.
In this talk I will present AND/OR search graphs, a recently introduced principle for decomposing search spaces for graphical models, which are closely related to BDD-trees and is thus superior to OBDDs. Specifically, minimal AND/OR graphs are canonical, their size is exponentially bounded by the graph-model.s tree-width, and they can be generated in time exponential in the tree-width using advanced cache-based search algorithms.