Jean-Marie Lagniez

Improving glucose for incremental SAT solving with assumptions: application to MUS extraction

Beside the important progresses observed in SAT solving, a number of applications explicitly rely on incremental SAT solving only. In this paper, we focus on refining the incremental SAT Solver Glucose, from the SAT engine perspective, and address a number of unseen problems this new use of SAT solvers opened. By playing on clause database cleaning, assumptions managements and other classical parameters, we show that our approach immediately and significantly improves an intensive assumption-based incremental SAT solving task: Minimal Unsatisfiable Set. We believe this work could bring immediate benefits in a number of other applications relying on incremental SAT.

On freezing and reactivating learnt clauses

In this paper, we propose a new dynamic management policy of the learnt clause database in modern SAT solvers. It is based on a dynamic freezing and activation principle of the learnt clauses. At a given search state, using a relevant selection function, it activates the most promising learnt clauses while freezing irrelevant ones. In this way, clauses learned at previous steps can be frozen at the current step and might be activated again in future steps of the search process. Our strategy tries to exploit pieces of information gathered from the past to deduce the relevance of a given clause for the remaining search steps. This policy contrasts with all the well-known deletion strategies, where a given learned clause is definitely eliminated. Experiments on SAT instances taken from the last competitions demonstrate the efficiency of our proposed technique.

Factoring out assumptions to speed up MUS extraction

In earlier work on a limited form of extended resolution for CDCL based SAT solving, new literals were introduced to factor out parts of learned clauses. The main goal was to shorten clauses, reduce proof size and memory usage and thus speed up propagation and conflict analysis. Even though some reduction was achieved, the effectiveness of this technique was rather modest for generic SAT solving. In this paper we show that factoring out literals is particularly useful for incremental SAT solving, based on assumptions. This is the most common approach for incremental SAT solving and was pioneered by the authors of MINISAT. Our first contribution is to focus on factoring out only assumptions, and actually all eagerly. This enables the use of compact dedicated data structures, and naturally suggests a new form of clause minimization, our second contribution. As last main contribution, we propose to use these data structures to maintain a partial proof trace for learned clauses with assumptions, which gives us a cheap way to flush useless learned clauses. In order to evaluate the effectiveness of our techniques we implemented them within the version of MINISAT used in the publically available state-of-the-art MUS extractor MUSer. An extensive experimental evaluation shows that factoring out assumptions in combination with our novel clause minimization procedure and eager clause removal is particularly effective in reducing average clause size, improves running time and in general the state-of-the-art in MUS extraction.

Revisiting clause exchange in parallel SAT solving

Managing learnt clause database is known to be a tricky task in SAT solvers. In the portfolio framework, the collaboration between threads through learnt clause exchange makes this problem even more difficult to tackle. Several techniques have been proposed in the last few years, but practical results are still in favor of very limited collaboration, or even no collaboration at all. This is mainly due to the difficulty that each thread has to manage a large amount of learnt clauses generated by the other workers. In this paper, we propose new efficient techniques for clause exchanges within a parallel SAT solver. In contrast to most of the current clause exchange methods, our approach relies on both export and import policies, and makes use of recent techniques that proves very effective in the sequential case. Extensive experimentations show the practical interest of the proposed ideas.

Boosting local search thanks to CDCL

In this paper, a novel hybrid and complete approach for propositional satisfiability, called SATHYS (Sat Hybrid Solver), is introduced. It efficiently combines the strength of both local search and CDCL based SAT solvers. Considering the consistent partial assignment under construction by the CDCL SAT solver, local search is used to extend it to a model of the Boolean formula, while the CDCL component is used by the local search one as a strategy to escape from a local minimum. Additionally, both solvers heavily cooperate thanks to relevant information gathered during search. Experimentations on SAT instances taken from the last competitions demonstrate the efficiency and the robustness of our hybrid solver with respect to the state-of-the-art CDCL based, local search and hybrid SAT solvers.

CoQuiAAS: A Constraint-Based Quick Abstract Argumentation Solver

Nowadays, argumentation is a salient keyword in artificial intelligence. The use of argumentation techniques is particularly convenient for thematics such that multiagent systems, where it allows to describe dialog protocols (using persuasion, negotiation, ...) or on-line discussion analysis, it also allows to handle queries where a single agent has to reason with conflicting information (inference in the presence of inconsistency, inconsistency measure). This very rich framework gives numerous reasoning tools, thanks to several acceptability semantics and inference policies. On the other hand, the progress of SAT solvers in the recent years, and more generally the progress on Constraint Programming paradigms, lead to some powerful approaches that permit to tackle theoretically hard problems. The needs of efficient applications to solve the usual reasoning tasks in argumentation, together with the capabilities of modern Constraint Programming solvers, lead us to study the encoding of usual acceptability semantics into logical settings. We propose diverse use of Constraint Programming techniques to develop a software library dedicated to argumentative reasoning. We present a library which offers the advantages to be generic and easily adaptable. We finally describe an experimental study of our approach for a set of semantics and inference tasks, and we describe the behaviour of our solver during the First International Competition on Computational Models of Argumentation.

Learning in Local Search

In this paper a learning based local search approach for propositional satisfiability is presented. It is based on an original adaptation of the conflict driven clause learning (CDCL) scheme to local search. First an extended implication graph for complete assignments of the set of variables is proposed. Secondly, a unit propagation based technique for building and using such implication graph is designed. Finally, we show how this new learning scheme can be integrated to the state-of-the-art local search solver WSAT. Interestingly enough, the obtained local search approach is able to prove unsatisfiability. Experimental results show very good performances on many classes of SAT instances from the last SAT competitions.

Questioning the Importance of WCORE-Like Minimization Steps in MUC-Finding Algorithms

When a constraint network is unsatisfiable, it can be of prime importance to provide the network designer with a full-fledged explanation of what causes the absence of any solution to the network. In this respect, minimal unsatisfiable cores (in short, MUCs) form the basis for such an explanation. Efficient MUC extractors are often made of an initial incomplete minimization step that delivers an upper-approximation of a MUC, followed by a refinement step. The first step is assumed crucial for the performance of the whole approach. In this paper, its actual importance is investigated. Especially, it is shown that the first step can be skipped when the refinement process dynamically exploits the information that this latter treatment itself entails.

A CSP solver focusing on FAC variables

The contribution of this paper is twofold. On the one hand, it introduces a concept of FAC variables in discrete Constraint Satisfaction Problems (CSPs). FAC variables can be discovered by local search techniques and powerfully exploited by MAC-based methods. On the other hand, a novel synergetic combination schema between local search paradigms, generalized arcconsistency and MAC-based algorithms is presented. By orchestrating a multiple-way flow of information between these various fully integrated search components, it often proves more competitive than the usual techniques on most classes of instances.

A Recursive Shortcut for CEGAR: Application To The Modal Logic K Satisfiability Problem

Counter-Example-Guided Abstraction Refinement (CEGAR) has been very successful in model checking. Since then, it has been applied to many different problems. It is especially proved to be a highly successful practical approach for solving the PSPACE complete QBF problem. In this paper, we propose a new CEGAR-like approach for tackling PSPACE complete problems that we call RECAR (Recursive Explore and Check Abstraction Refinement). We show that this generic approach is sound and complete. Then we propose a specific implementation of the RECAR approach to solve the modal logic K satisfiability problem. We implemented both CEGAR and RECAR approaches for the modal logic K satisfiability problem within the solver MoSaiC. We compared experimentally those approaches to the state-of-the-art solvers for that problem. The RECAR approach outperforms the CEGAR one for that problem and also compares favorably against the state-of-the-art on the benchmarks considered.