Enhancing Search-Based QBF Solving by Dynamic Blocked Clause Elimination

Florian Lonsing, Fahiem Bacchus, Armin Biere, Uwe Egly, Martina Seidl

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Among preprocessing techniques for quantified Boolean formula (QBF) solving, quantified blocked clause elimination (QBCE) has been found to be extremely effective. We investigate the power of dynamically applying QBCE in search-based QBF solving with clause and cube learning (QCDCL). This dynamic application of QBCE is in sharp contrast to its typical use as a mere preprocessing technique. In our dynamic approach, QBCE is applied eagerly to the formula interpreted under the assignments that have been enumerated in QCDCL. The tight integration of QBCE in QCDCL results in a variant of cube learning which is exponentially stronger than the traditional method. We implemented our approach in the QBF solver DepQBF and ran experiments on instances from the QBF Gallery 2014. On application benchmarks, QCDCL with dynamic QBCE substantially outperforms traditional QCDCL. Moreover, our approach is compatible with incremental solving and can be combined with preprocessing techniques other than QBCE.
Original languageEnglish
Title of host publicationProc. 20th Intl. Conf. on Logic for Programming , Artificial Intelligence, and Reasoning 2015
PublisherSpringer
Pages418-433
Number of pages16
Volume9450
Publication statusPublished - Dec 2015

Publication series

NameLecture Notes in Computer Science (LNCS)

Fields of science

  • 102 Computer Sciences
  • 603109 Logic
  • 202006 Computer hardware

JKU Focus areas

  • Computation in Informatics and Mathematics

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