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Quantifier Elimination in Stochastic Boolean Satisfiability

Authors Hao-Ren Wang, Kuan-Hua Tu, Jie-Hong Roland Jiang, Christoph Scholl



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Author Details

Hao-Ren Wang
  • Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
Kuan-Hua Tu
  • Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
Jie-Hong Roland Jiang
  • Graduate Institute of Electronics Engineering / Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
Christoph Scholl
  • Department of Computer Science, Universität Freiburg, Germany

Cite AsGet BibTex

Hao-Ren Wang, Kuan-Hua Tu, Jie-Hong Roland Jiang, and Christoph Scholl. Quantifier Elimination in Stochastic Boolean Satisfiability. In 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 236, pp. 23:1-23:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.SAT.2022.23

Abstract

Stochastic Boolean Satisfiability (SSAT) generalizes quantified Boolean formulas (QBFs) by allowing quantification over random variables. Its generality makes SSAT powerful to model decision or optimization problems under uncertainty. On the other hand, the generalization complicates the computation in its counting nature. In this work, we address the following two questions: 1) Is there an analogy of quantifier elimination in SSAT, similar to QBF? 2) If quantifier elimination is possible for SSAT, can it be effective for SSAT solving? We answer them affirmatively, and develop an SSAT decision procedure based on quantifier elimination. Experimental results demonstrate the unique benefits of the new method compared to the state-of-the-art solvers.

Subject Classification

ACM Subject Classification
  • Theory of computation → Automated reasoning
  • Theory of computation → Constraint and logic programming
Keywords
  • Stochastic Boolean Satisfiability
  • Quantifier Elimination
  • Decision Procedure

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References

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