CadiBack: Extracting Backbones with CaDiCaL

The backbone of a satisfiable formula is the set of literals that are true in all its satisfying assignments. Backbone computation can improve a wide range of SAT-based applications, such as verification, fault localization and product configuration. In this tool paper, we introduce a new backbone extraction tool called CadiBack. It takes advantage of unique features available in our state-of-the-art SAT solver CaDiCaL including transparent inprocessing and single clause assumptions, which have not been evaluated in this context before. In addition, CaDiCaL is enhanced with an improved algorithm to support model rotation by utilizing watched literal data structures. In our comprehensive experiments with a large number of benchmarks, CadiBack solves 60% more instances than the state-of-the-art backbone extraction tool MiniBones. Our tool is thoroughly tested with fuzzing, internal correctness checking and cross-checking on a large benchmark set. It is publicly available as open source, well documented and easy to extend

Stratified Certification for k-Induction

Our recently proposed certification framework for bit-level k-induction-based model checking has been shown to be quite effective in increasing the trust of verification results even though it partially involved quantifier reasoning. In this paper we show how to simplify the approach by assuming reset functions to be stratified. This way it can be lifted to word-level and in principle to other theories where quantifier reasoning is difficult. Our new method requires six simple SAT checks and one polynomial-time check, allowing certification to remain in co-NP while the previous approach required five SAT checks and one QBF check. Experimental results show a substantial performance gain for our new approach. Finally we present and evaluate our new tool CERTIFAIGER-WL which is able to certify k-induction-based word-level model checking.Peer reviewe

SAT Competition 2020

The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition

SAT Competition 2020

The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition. (C) 2021 The Authors. Published by Elsevier B.V.Peer reviewe

Using an Algorithm Portfolio to Solve Sokoban

The game of Sokoban is an interesting platform for algorithm research. It is hard for humans and computers alike. Even small levels can take a lot of computation for all known algorithms. In this paper we will describe how a search based Sokoban solver can be structured and which algorithms can be used to realize each critical part. We implement a variety of those, construct a number of different solvers and combine them into an algorithm portfolio. The solver we construct this way can outperform existing solvers when run in parallel, that is, our solver with 16 processors outperforms the previous sequential solvers

Single Clause Assumption without Activation Literals to Speed-up IC3

272765Austrian Science Fund (FWF)Austrian Science Fund (FWF

PASAR — Planning as Satisfiability with Abstraction Refinement

One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results

Stratified Certification for k-Induction

Our recently proposed certification framework for bit-level k-induction-based model checking has been shown to be quite effective in increasing the trust of verification results even though it partially involved quantifier reasoning. In this paper we show how to simplify the approach by assuming reset functions to be stratified. This way it can be lifted to word-level and in principle to other theories where quantifier reasoning is difficult. Our new method requires six simple SAT checks and one polynomial-time check, allowing certification to remain in co-NP while the previous approach required five SAT checks and one QBF check. Experimental results show a substantial performance gain for our new approach. Finally we present and evaluate our new tool CERTIFAIGER-WL which is able to certify k-induction-based word-level model checking.Peer reviewe