Papers by Jean-Marie Lagniez
Data & Knowledge Engineering
Proceedings of the AAAI Conference on Artificial Intelligence
This paper is concerned with preprocessing techniques for propositional model counting. We have i... more This paper is concerned with preprocessing techniques for propositional model counting. We have implemented a preprocessor which includes many elementary preprocessing techniques, including occurrence reduction, vivification, backbone identification, as well as equivalence, AND and XOR gate identification and replacement. We performed intensive experiments, using a huge number of benchmarks coming from a large number of families. Two approaches to model counting have been considered downstream: ”direct” model counting using Cachet and compilation-based model counting, based on the C2D compiler. The experimental results we have obtained show that our preprocessor is both efficient and robust.
Proceedings of the AAAI Conference on Artificial Intelligence
We present a SAT-based approach for solving the modal logic S5-satisfiability problem. That probl... more We present a SAT-based approach for solving the modal logic S5-satisfiability problem. That problem being NP-complete, the translation into SAT is not a surprise. Our contribution is to greatly reduce the number of propositional variables and clauses required to encode the problem. We first present a syntactic property called diamond degree. We show that the size of an S5-model satisfying a formula phi can be bounded by its diamond degree. Such measure can thus be used as an upper bound for generating a SAT encoding for the S5-satisfiability of that formula. We also propose a lightweight caching system which allows us to further reduce the size of the propositional formula.We implemented a generic SAT-based approach within the modal logic S5 solver S52SAT. It allowed us to compare experimentally our new upper-bound against previously known one, i.e. the number of modalities of phi and to evaluate the effect of our caching technique. We also compared our solver againstexisting modal ...
Proceedings of the AAAI Conference on Artificial Intelligence
The concepts of MSS (Maximal Satisfiable Subset) andCoMSS (also called Minimal Correction Subset)... more The concepts of MSS (Maximal Satisfiable Subset) andCoMSS (also called Minimal Correction Subset) playa key role in many A.I. approaches and techniques. Inthis paper, a novel algorithm for partitioning a BooleanCNF formula into one MSS and the correspondingCoMSS is introduced. Extensive empirical evaluationshows that it is more robust and more efficient on mostinstances than currently available techniques.
Proceedings of the AAAI Conference on Artificial Intelligence
The efficient extraction of one maximal information subset that does not conflict with multiple c... more The efficient extraction of one maximal information subset that does not conflict with multiple contxts or additional information sources is a key basic issue in many A.I. domains, especially when these contexts or sources can be mutually conflicting. In this paper, this question is addressed from a computational point of view in clausal Boolean logic. A new approach is introduced that experimentally outperforms the currently most efficient technique.
Proceedings of the AAAI Conference on Artificial Intelligence
An original method for the extraction of one maximal subset of a set of Boolean clauses that must... more An original method for the extraction of one maximal subset of a set of Boolean clauses that must be satisfiable with possibly mutually contradictory assumptive contexts is motivated and experimented. Noticeably, it performs a direct computation and avoids the enumeration of all subsets that are satisfiable with at least one of the contexts. The method applies for subsets that are maximal with respect to inclusion or cardinality.
A constraint network P is a pair (X, V) where X a finite set of m constraints over a finite set V... more A constraint network P is a pair (X, V) where X a finite set of m constraints over a finite set V of discrete variables. Each variable V ∈ V has its own instantiation domain, denoted dom(V). Each constraint C ∈ X involves a subset of variables of V, called scope and noted var(C). A relation, dehal-00866342,
A method is proposed to enforce specific solutions in constraint networks. Contrary to previous a... more A method is proposed to enforce specific solutions in constraint networks. Contrary to previous approaches, it yields a set of constraints to be dropped whose cardinality is minimal.
SAT solvers have become efficient for solving NP-complete problems (and beyond). Usually those pr... more SAT solvers have become efficient for solving NP-complete problems (and beyond). Usually those problems are solved by direct translation to SAT or by solving iteratively SAT problems in a procedure like CEGAR. Recently, a new recur-sive CEGAR loop working with two abstraction levels, called RECAR, was proposed and instantiated for modal logic K. We aim to complete this work for modal logics based on axioms (B), (D), (T), (4) and (5). Experimental results show that the approach is competitive against state-of-the-art solvers for modal logics K, KT and S4.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
The enumeration of all Maximal Satisfiable Subsets (MSSes) or all Minimal Correction Subsets (MCS... more The enumeration of all Maximal Satisfiable Subsets (MSSes) or all Minimal Correction Subsets (MCSes) of an unsatisfiable CNF Boolean formula is a useful and sometimes necessary step for solving a variety of important A.I. issues. Although the number of different MCSes of a CNF Boolean formula is exponential in the worst case, it remains low in many practical situations; this makes the tentative enumeration possibly successful in these latter cases. In the paper, a technique is introduced that boosts the currently most efficient practical approaches to enumerate MCSes. It implements a model rotation paradigm that allows the set of MCSes to be computed in an heuristically efficient way.
Proceedings of the AAAI Conference on Artificial Intelligence
We present SAT encoding schemes for distance-based belief merging operators relying on the (possi... more We present SAT encoding schemes for distance-based belief merging operators relying on the (possibly weighted) drastic distance or the Hamming distance between interpretations, and using sum, GMax (leximax) or GMin (leximin) as aggregation function. In order to evaluate these encoding schemes, we generated benchmarks of a time-tabling problem and translated them into belief merging instances. Then, taking advantage of these schemes, we compiled the merged bases of the resulting instances into query-equivalent CNF formulae. Experiments have shown the benefits which can be gained by considering the SAT encoding schemes we pointed out. Especially, thanks to them, we succeeded in computing query-equivalent formulae for merging instances based on hundreds of variables, which are out of reach of previous implementations.
This paper is concerned with local search techniques (LS) for solving CSPs (Constraint Satisfacti... more This paper is concerned with local search techniques (LS) for solving CSPs (Constraint Satisfaction Problems). An efficient data structure is presented that allows the performance of LS to be boosted. Experimentations on benchmarks from the last international CSP competitions illustrate its very positive impact. It has been implemented in wcsp δ : an efficient open-ended and open-source local search platform for CSP that can accommodate various meta-heuristics.
principles and practice of constraint programming, 2016
International Joint Conference on Artificial Intelligence, 2015
We present and evaluate a top-down algorithm for compiling finite-domain constraint networks (CNs... more We present and evaluate a top-down algorithm for compiling finite-domain constraint networks (CNs) into the language MDDG of multivalued decomposable decision graphs. Though it includes Decision-DNNF as a proper subset, MDDG offers the same key tractable queries and transformations as Decision-DNNF, which makes it useful for many applications. Intensive experiments showed that our compiler cn2mddg succeeds in compiling CNs which are out of the reach of standard approaches based on a translation of the input network to CNF, followed by a compilation to Decision-DNNF. Furthermore, the sizes of the resulting compiled representations turn out to be much smaller (sometimes by several orders of magnitude).
Abstract—This paper provides a short system description of our new portfolio-based solver called ... more Abstract—This paper provides a short system description of our new portfolio-based solver called PeneLoPe, based on ManySat. Particularly, this solver focuses on collaboration between threads, providing different policies for exporting and importing learnt clauses between CDCL searches. Moreover, different restart strategies are also available, together with a deterministic mode. I. OVERVIEW PeneLoPe is a portfolio parallel SAT solver that uses the most effective techniques proposed in the sequential frame-work: unit propagation, lazy data structures, activity-based heuristics, progress saving for polarities, clause learning, etc. As for most of existing solvers, a first preprocessing step is achieved. For this step-which is typically sequential- we have chosen to make use of SatElite [3].
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Papers by Jean-Marie Lagniez