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Related Experiment Video

Updated: Jan 24, 2026

A High Resolution Method to Monitor Phosphorylation-dependent Activation of IRF3

A High Resolution Method to Monitor Phosphorylation-dependent Activation of IRF3

Published on: January 24, 2016

Long-Distance Q-Resolution with Dependency Schemes.

Tomáš Peitl¹, Friedrich Slivovsky¹, Stefan Szeider¹

¹Algorithms and Complexity Group, TU Wien, 1040 Vienna, Austria.

Journal of Automated Reasoning

|May 21, 2019

Summary

This summary is machine-generated.

We introduce long-distance Q(D)-resolution, a sound proof system for quantified Boolean formulas (QBFs). This system enables polynomial-time strategy extraction, enhancing the reliability of QBF solvers.

Keywords:

Dependency schemes Q-resolution QBF Strategy extraction

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Area of Science:

Formal verification
Automated reasoning
Theoretical computer science

Background:

Resolution proof systems are crucial for analyzing quantified Boolean formula (QBF) solvers.
State-of-the-art QBF solvers utilize these systems to generate proofs.

Purpose of the Study:

To investigate a novel proof system combining Q-resolution with generalized universal reduction and long-distance Q-resolution.
To establish the soundness and efficiency of this new system for QBF solving.

Main Methods:

Combining Q-resolution with generalized universal reduction and long-distance Q-resolution.
Developing a general result for polynomial-time strategy extraction across various dependency schemes.
Analyzing the proof generation process of search-based QBF solvers.

Main Results:

Introduced long-distance Q(D)-resolution, a sound proof system for reflexive resolution-path dependency schemes.
Demonstrated polynomial-time strategy extraction for long-distance Q(D)-resolution under a broad class of dependency schemes.
Showed that search-based QBF solvers with learning generate long-distance Q(D)-resolution proofs, ensuring partial soundness.

Conclusions:

Long-distance Q(D)-resolution offers enhanced reliability and efficiency for QBF solving.
The findings provide theoretical underpinnings for improving the performance and trustworthiness of automated QBF solvers.
Experimental results with DepQBF validate the practical applicability of the developed proof system.