Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple properties while maintaining similarity to input molecule. Existing generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing properties. To address such challenges, we pro...

In the latest years and with the advancements of the multicore computing world, the constraint programming community tried to benefit from the capacity of new machines and make the best use of them through several parallel schemes for constraint solving. In this paper, we present a new approach using parallel consistencies to enhance the classic solving process. In fact, a master process tries ...

The integration of methods of Constraint Programming and Multi-Agent-Systems is discussed in this paper. We describe different agent topologies for Constraint Satisfaction Problems and discuss their properties with a special focus on dynamic and distributed settings. This motivates our new architecture for constraint processing with reactive agents. The resulting systems are very flexible and c...

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among ...

The availability of commodity multi-core and multi-processor machines and the inherent parallelism in constraint programming search offer significant opportunities for constraint programming. They also present a fundamental challenge: how to exploit parallelism transparently to speed up constraint programs. This paper shows how to parallelize constraint programs transparently without changes to...