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

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

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

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

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

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

Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks

Minimal trap spaces (MTSs) capture subspaces in which the Boolean dynamics is trapped, whatever the update mode. They correspond to the attractors of the most permissive mode. Due to their versatility, the computation of MTSs has recently gained traction, essentially by focusing on their enumeration. In this paper, we address the logical reasoning on universal properties of MTSs in the scope of two problems: the reprogramming of Boolean networks for identifying the permanent freeze of Boolean variables that enforce a given property on all the MTSs, and the synthesis of Boolean networks from universal properties on their MTSs. Both problems reduce to solving the satisfiability of quantified propositional logic formula with 3 levels of quantifiers ($\exists\forall\exists$). In this paper, we introduce a Counter-Example Guided Refinement Abstraction (CEGAR) to efficiently solve these problems by coupling the resolution of two simpler formulas. We provide a prototype relying on Answer-Set Programming for each formula and show its tractability on a wide range of Boolean models of biological networks.Comment: Accepted at 21st International Conference on Computational Methods in Systems Biology (CMSB 2023