Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca
This document represents the proceedings of the 2022 XCSP3 Competition. The results of this competition of constraint solvers were presented at FLOC (Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from 31th July 2022 to 7th August, 2022.
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca
This document represents the proceedings of the 2025 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'25 (31st International Conference on Principles and Practice of Constraint Programming).
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca
This document represents the proceedings of the 2024 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'24 (30th International Conference on Principles and Practice of Constraint Programming).
Jean-Marie Lagniez, Emmanuel Lonca, Jean-Guy Mailly et al.
Since 2015, the International Competition on Computational Models of Argumentation (ICCMA) provides a systematic comparison of the different algorithms for solving some classical reasoning problems in the domain of abstract argumentation. This paper discusses the design of the Fourth International Competition on Computational Models of Argumentation. We describe the rules of the competition and the benchmark selection method that we used. After a brief presentation of the competitors, we give an overview of the results.
Daniel Le Berre, Emmanuel Lonca, Pierre Marquis
Optimization is a key task in a number of applications. When the set of feasible solutions under consideration is of combinatorial nature and described in an implicit way as a set of constraints, optimization is typically NP-hard. Fortunately, in many problems, the set of feasible solutions does not often change and is independent from the user's request. In such cases, compiling the set of constraints describing the set of feasible solutions during an off-line phase makes sense, if this compilation step renders computationally easier the generation of a non-dominated, yet feasible solution matching the user's requirements and preferences (which are only known at the on-line step). In this article, we focus on propositional constraints. The subsets L of the NNF language analyzed in Darwiche and Marquis' knowledge compilation map are considered. A number of families F of representations of objective functions over propositional variables, including linear pseudo-Boolean functions and more sophisticated ones, are considered. For each language L and each family F, the complexity of generating an optimal solution when the constraints are compiled into L and optimality is to be considered w.r.t. a function from F is identified.