Jean-Marie Lagniez

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Abstract. 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(More)
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(More)
The concepts of MSS (Maximal Satisfiable Subset) and CoMSS (also called Minimal Correction Subset) play a key role in many A.I. approaches and techniques. In this paper, a novel algorithm for partitioning a Boolean CNF formula into one MSS and the corresponding CoMSS is introduced. Extensive empirical evaluation shows that it is more robust and more(More)
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(More)
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(More)
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(More)
Counting the models of a propositional formula is a key issue for a number of AI problems, but few propositional languages offer the possibility to count models efficiently. In order to fill the gap, we introduce the language EADT of (extended) affine decision trees. An extended affine decision tree simply is a tree with affine decision nodes and some(More)