Learning constraint networks is known to require a number of membership queries
exponential in the number of variables. In this paper, we learn constraint networks by asking …

Many natural optimization problems are NP-hard, which implies that they are probably hard
to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for …

Background: To solve the cluster analysis better, we propose a new method based on the
chaotic particle swarm optimization (CPSO) algorithm. Methods: In order to enhance the …

Integrating machine learning with automated reasoning is one of the major goals of modern
AI systems. In this paper, we propose a non-fully-differentiable architecture that is able to …

Many real-world problems can be effectively solved by means of combinatorial optimization.
However, appropriate models to give to a solver are not always available, and sometimes …

Abstract The Maximum Satisfiability (MaxSAT), an important optimization problem, has a
range of applications, including network routing, planning and scheduling, and …

Constraint acquisition can assist non-expert users to model their problems as constraint
networks. In active constraint acquisition, this is achieved through an interaction between the …

Constraint programming and AI planning are powerful tools for solving assignment,
optimization, and scheduling problems. They require, however, the rarely available …

Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and
operations research to model complex decision problems like scheduling and routing …

The remarkable achievements of machine learning techniques in analyzing discrete
structures have drawn significant attention towards their integration into combinatorial …