We study the partitioning of temporal planning problems formulated as mixed-integer nonlinear programming problems, develop methods to reduce the search space of partitioned subpr...
We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constr...
Julien Legriel, Colas Le Guernic, Scott Cotton, Od...
The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit sy...
We introduce a new multi-threaded parsing algorithm on unification grammars designed specifically for multimodal interaction and noisy environments. By lifting some traditional co...
We propose an environment for musical constraint solving, in the visual programming language OpenMusic. We describe an implementation of a local search algorithm, called adaptive s...