Frits Vaandrager

Véronique Bruyère, Bharat Garhewal, Guillermo A. Pérez et al.

We present the first algorithm for query learning Mealy machines with timers in a black-box context. Our algorithm is an extension of the L# algorithm of Vaandrager et al. to a timed setting. We rely on symbolic queries which empower us to reason on untimed executions while learning. Similarly to the algorithm for learning timed automata of Waga, these symbolic queries can be realized using finitely many concrete queries. Experiments with a prototype implementation show that our algorithm is able to efficiently learn realistic benchmarks.

Alexis Linard, Rick Smetsers, Frits Vaandrager et al.

A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related - yet seemingly novel - problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers.