We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
"Constraint satisfaction is a general problem in which the goal is to find values for a set of variables that will satisfy a given set of constraints. It is the core of many a...
Stochastic dependency parsers can achieve very good results when they are trained on large corpora that have been manually annotated. Active learning is a procedure that aims at r...
Most of the previous work on non-invasive brain-computer interfaces (BCIs) has been focused on feature extraction and classification algorithms to achieve high performance for the...
Several propositional fragments have been considered so far as target languages for knowledge compilation and used for improving computational tasks from major AI areas (like infe...
Sylvie Coste-Marquis, Daniel Le Berre, Florian Let...