We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Abstract. Existing algorithms for regular inference (aka automata learning) allows to infer a finite state machine by observing the output that the machine produces in response to ...
We propose a method for inferring parameterized regular types for logic programs as solutions for systems of constraints over sets of finite ground Herbrand terms (set constraint s...
Francisco Bueno, Jorge A. Navas, Manuel V. Hermene...
In a previous paper we showed that a random 4-regular graph asymptotically almost surely (a.a.s.) has chromatic number 3. Here we extend the method to show that a random 6-regular...
We study the alternating-time temporal logics ATL and ATL extended with strategy contexts: these make agents commit to their strategies during the evaluation of formulas, contrary...