Simone Heisinger’s scientific contributions

Modern solvers for quantified Boolean formulas (QBFs) process formulas in prenex form, which divides each QBF into two parts: the quantifier prefix and the propositional matrix. While this representation does not cover the full language of QBF, every non-prenex formula can be transformed to an equivalent formula in prenex form. This transformation offers several degrees of freedom and blurs structural information that might be useful for the solvers. In a case study conducted 20 years back, it has been shown that the applied transformation strategy heavily impacts solving time. We revisit this work and investigate how sensitive recent QBF solvers perform w.r.t. various prenexing strategies.

Recent approaches on verification and reasoning solve SAT and QBF encodings using state-of-the-art SMT solvers, as it “makes implementation much easier”. The ease-of-use of these solvers make SAT and QBF solvers less visible to users of solvers—who are maybe from different research communities—potentially not exploiting the power of state-of-the-art tools. In this work, we motivate the need to build bridges over the widening solver-gap and introduce Booleguru , a tool to convert between formats for logic formulas. It makes SAT and QBF solvers more accessible by using techniques known from SMT solvers, such as advanced Python interfaces like Z3Py and easily generatable languages like SMT-LIB, integrating them to our conversion tool. We then introduce a language to manipulate and combine multiple formulas, optionally applying transformations for quickly prototyping encodings. Booleguru ’s advanced scripting capabilities form a programming environment specialized for Boolean logic, offering a more efficient way to develop novel problem encodings.

As the application of quantified Boolean formulas (QBF) continues to expand in various scientific and industrial domains, the development of efficient QBF solvers and their underlying proving strategies is of growing importance. To understand and to compare different solving approaches, techniques of proof complexity are applied. To this end, formula families have been crafted that exhibit certain properties of proof systems. These formulas are valuable to test and compare specific solver implementations. Traditionally, the focus is on false formulas, in this work we extend the formula generator QBFFam to produce true formulas based on two popular formula families from proof complexity. KeywordsQBFSolverBenchmarkingKBKFQParity