138 research outputs found
Integrating Conflict Driven Clause Learning to Local Search
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions
Preserving Partial Solutions while Relaxing Constraint Networks
International audienceThis paper is about transforming constraint net- works to accommodate additional constraints in specific ways. The focus is on two intertwined issues. First, we investigate how partial solutions to an initial network can be preserved from the potential impact of additional constraints. Second, we study how more permissive constraints, which are intended to enlarge the set of solutions, can be accommodated in a constraint network. These two problems are studied in the general case and the light is shed on their relationship. A case study is then investigated where a more permissive additional constraint is taken into account through a form of network relaxation, while some previous partial solutions are preserved at the same time
A CSP solver focusing on FAC variables
International audienceThe contribution of this paper is twofold. On the one hand, it introduces a concept of FAC variables in discrete Constraint Satisfaction Prob- lems (CSPs). FAC variables can be discovered by local search techniques and powerfully exploited by MAC-based methods. On the other hand, a novel syn- ergetic combination schema between local search paradigms, generalized arc- consistency and MAC-based algorithms is presented. By orchestrating a multiple- way flow of information between these various fully integrated search compo- nents, it often proves more competitive than the usual techniques on most classes of instances
On freezeing and reactivating learnt clauses
Best paper awardInternational audienceIn 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
Relax!
International audienceThis paper is concerned with a form of relaxation of constraint networks. The focus is on situations where additional constraints are intended to extend a non- empty set of preexisting solutions. These constraints require a speci c treatment since merely inserting them inside the network would lead to their preemption by more restrictive ones. Several approaches to handle these additional constraints are investigated from con- ceptual and experimental points of view
SatHYS: Sat Hybrid Solver
Proceedings of SAT Race 2010: Solver and Benchmarks DescriptionsThis note describes features of the version of SATHYS that entered the SAT-race 2010 affiliated to the SAT'2010 conference in Edinburgh, Scotland, UK
Analyse de conflits dans le cadre de la recherche locale
International audienceDans cet article, nous présentons une nouvelle approche pour sortir des minimums locaux dans le cadre de la recherche locale. Cette approche est basée sur le principe d'analyse de conflits utilisé dans les solveurs SAT modernes. Nous proposons une extension du graphe d'implications au cadre de la recherche locale où plusieurs conflits sont présents pour une interprétation donnée. Nous présentons ensuite une méthode basée sur la propagation unitaire, permettant de construire et d'exploiter de tels graphes. Enfin, nous étendons le schéma classique de WSAT pour y intégrer notre analyse de conflits. Les résultats expérimentaux montrent que l'intégration de notre système d'analyse de conflits améliore sensiblement les performances de WSAT sur les problèmes structurés. De plus, cette méthode isolant des sous-problèmes inconsistants, est capable de montrer que l'instance n'admet pas de modèle
Automated Benchmarking of Incremental SAT and QBF Solvers
Incremental SAT and QBF solving potentially yields improvements when
sequences of related formulas are solved. An incremental application is usually
tailored towards some specific solver and decomposes a problem into incremental
solver calls. This hinders the independent comparison of different solvers,
particularly when the application program is not available. As a remedy, we
present an approach to automated benchmarking of incremental SAT and QBF
solvers. Given a collection of formulas in (Q)DIMACS format generated
incrementally by an application program, our approach automatically translates
the formulas into instructions to import and solve a formula by an incremental
SAT/QBF solver. The result of the translation is a program which replays the
incremental solver calls and thus allows to evaluate incremental solvers
independently from the application program. We illustrate our approach by
different hardware verification problems for SAT and QBF solvers.Comment: camera-ready version (8 pages + 2 pages appendix), to appear in the
proceedings of the 20th International Conference on Logic for Programming,
Artificial Intelligence and Reasoning (LPAR), LNCS, Springer, 201
Computing Abductive Explanations for Boosted Trees
Boosted trees is a dominant ML model, exhibiting high accuracy. However,
boosted trees are hardly intelligible, and this is a problem whenever they are
used in safety-critical applications. Indeed, in such a context, rigorous
explanations of the predictions made are expected. Recent work have shown how
subset-minimal abductive explanations can be derived for boosted trees, using
automated reasoning techniques. However, the generation of such well-founded
explanations is intractable in the general case. To improve the scalability of
their generation, we introduce the notion of tree-specific explanation for a
boosted tree. We show that tree-specific explanations are abductive
explanations that can be computed in polynomial time. We also explain how to
derive a subset-minimal abductive explanation from a tree-specific explanation.
Experiments on various datasets show the computational benefits of leveraging
tree-specific explanations for deriving subset-minimal abductive explanations
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