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Classes of Hard Formulas for QBF Resolution

Schleitzer, Agnes (a:1:{s:5:"en_US";s:18:"University of Jena";}) | Beyersdorff, Olaf (Friedrich-Schiller-Universit¨at Jena, Fakult¨at f¨ur Mathematik und Informatik, Institut f¨ur Informatik)

Journal of Artificial Intelligence Research

To date, we know only a few handcrafted quantified Boolean formulas (QBFs) that are hard for central QBF resolution systems such as Q-Res and QU-Res, and only one specific QBF family to separate Q-Res and QU-Res. Here we provide a general method to construct hard formulas for Q-Res and QU-Res. The construction uses simple propositional formulas (e.g. minimally unsatisfiable formulas) in combination with easy QBF gadgets (Σb2 formulas without constant winning strategies). This leads to a host of new hard formulas, including new classes of hard random QBFs. We further present generic constructions for formulas separating Q-Res and QU-Res, and for separating Q-Res and LD-Q-Res.


Extension Variables in QBF Resolution

Beyersdorf, Olaf (University of Leeds) | Chew, Leroy (University of Leeds) | Janota, Mikolas (Microsoft Research, Cambridge)

AAAI Conferences

We investigate two QBF resolution systems that use extension variables: weak extended Q-resolution, where the extension variables are quantified at the innermost level, and extended Q-resolution, where the extension variables can be placed inside the quantifier prefix. These systems have been considered previously by Wintersteiger et al, who give experimental evidence that extended Q-resolution is stronger than weak extended Q-resolution. Here we prove an exponential separation between the two systems, thereby confirming the conjecture of Wintersteiger et al. Conceptually, this separation relies on showing strategy extraction for weak extended Q-resolution by bounded-depth circuits. In contrast, we show that this strong strategy extraction result fails in extended Q-resolution.