AND/OR search spaces for graphical models

  • Authors:

  • Rina Dechter;Robert Mateescu

  • Affiliations:

  • Donald Bren School of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA;Donald Bren School of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2007

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Abstract

The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometimes reduce the search space exponentially. Indeed, most algorithmic advances in search-based constraint processing and probabilistic inference can be viewed as searching an AND/OR search tree or graph. Familiar parameters such as the depth of a spanning tree, treewidth and pathwidth are shown to play a key role in characterizing the effect of AND/OR search graphs vs. the traditional OR search graphs. We compare memory intensive AND/OR graph search with inference methods, and place various existing algorithms within the AND/OR search space.