Empowering Mini-Bucket in Anytime Heuristic Search with Look-Ahead: Preliminary Evaluation

Authors

  • William Lam University of California, Irvine
  • Kalev Kask University of California, Irvine
  • Rina Dechter University of California, Irvine

DOI:

https://doi.org/10.1609/socs.v6i1.18367

Keywords:

graphical models, search, heuristics

Abstract

The paper explores the potential of look-ahead methods within the context of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., weighted CSPS or MAP inference). We study how these methods can be used to compensate for the approximation error of the initially generated Mini-Bucket heuristics, within the context of anytime Branch-And-Bound search.

SoCS 2015 Proceedings Cover

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Published

2021-09-01

Issue

Vol. 6 No. 1 (2015): Eighth Annual Symposium on Combinatorial Search

Section

Original Research Abstracts