A Robust Class of Data Languages and an Application to Learning

Benedikt Bollig (1, 2) , Peter Habermehl (3) , Martin Leucker (4) , Benjamin Monmege (5)

1 LSV - Laboratoire Spécification et Vérification [Cachan]
2 MEXICO - Modeling and Exploitation of Interaction and Concurrency
3 LIAFA - Laboratoire d'informatique Algorithmique : Fondements et Applications
4 ISP - Institut für Softwaretechnik und Programmiersprachen [Lübeck]
5 ULB - Département d'Informatique [Bruxelles]

We introduce session automata, an automata model to process data words, i.e., words over an infinite alphabet. Session automata support the notion of fresh data values, which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. Session automata have an expressiveness partly extending, partly reducing that of classical register automata. We show that, unlike register automata and their various extensions, session automata are robust: They (i) are closed under intersection, union, and (resource-sensitive) complementation, (ii) admit a symbolic regular representation, (iii) have a decidable inclusion problem (unlike register automata), and (iv) enjoy logical characterizations. Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries.

Mots clés: Data words; Register Automata; Freshness; Angluin-style learning

Domaines: Théorie et langage formel [cs.FL]

Benedikt Bollig, Peter Habermehl, Martin Leucker, Benjamin Monmege. A Robust Class of Data Languages and an Application to Learning. Logical Methods in Computer Science, 2014, 10 (4:19), ⟨10.2168/LMCS-10(4:19)2014⟩. ⟨hal-00920945v2⟩