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Local and Global Complete Solution Learning Methods for QBF

  • Ian P. Gent
  • Andrew G. D. Rowley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3569)

Abstract

Solvers for Quantified Boolean Formulae (QBF) use many analogues of technique from SAT. A significant amount of work has gone into extending conflict based techniques such as conflict learning to solution learning, which is irrelevant in SAT but can play a large role in success in QBF. Unfortunately, solution learning techniques have not been highly successful to date. We argue that one reason for this is that solution learning techniques have been ‘incomplete’. That is, not all the information implied in a solution is learnt. We introduce two new techniques for learning as much as possible from solutions, and we call them complete methods. The two methods contrast in how long they keep information. One, Complete Local Solution Learning, discards solutions on backtracking past a previous existential variable. The other, Complete Global Solution Learning, keeps solutions indefinitely. Our detailed experimental analysis suggests that both can improve search over standard solution learning, while the local method seems to offer a more suitable tradeoff than global learning.

Keywords

Indicator Variable Conjunctive Normal Form Boolean Formula Local Learning Universal Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ian P. Gent
    • 1
  • Andrew G. D. Rowley
    • 2
  1. 1.School of Computer ScienceUniversity of St. AndrewsSt. Andrews, FifeUK
  2. 2.Manchester ComputingManchesterUK

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