TY - GEN
T1 - Exploiting the hidden structure of junction trees for MPE
AU - Kenig, Batya
AU - Gal, Avigdor
N1 - Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).
PY - 2016
Y1 - 2016
N2 - The role of decomposition-trees (also known as junction and clique trees) in probabilistic inference is widely known and has been the basis for many well known inference algorithms. Recent approaches have demonstrated that such trees have a "hidden structure", which enables the characterization of tractable problem instances as well as lead to insights that enable boosting the performance of inference algorithms. We consider the MPE problem on a Boolean formula in CNF where each literal in the formula is associated with a weight. We describe techniques for exploiting the junction-tree structure of these formulas in the context of a branch-and-bound algorithm for MPE.
AB - The role of decomposition-trees (also known as junction and clique trees) in probabilistic inference is widely known and has been the basis for many well known inference algorithms. Recent approaches have demonstrated that such trees have a "hidden structure", which enables the characterization of tractable problem instances as well as lead to insights that enable boosting the performance of inference algorithms. We consider the MPE problem on a Boolean formula in CNF where each literal in the formula is associated with a weight. We describe techniques for exploiting the junction-tree structure of these formulas in the context of a branch-and-bound algorithm for MPE.
UR - http://www.scopus.com/inward/record.url?scp=85021903587&partnerID=8YFLogxK
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AN - SCOPUS:85021903587
T3 - AAAI Workshop - Technical Report
SP - 333
EP - 338
BT - WS-16-01
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 13 February 2016
ER -