Benedikt Bollig ; Peter Habermehl ; Martin Leucker ; Benjamin Monmege - A Robust Class of Data Languages and an Application to Learning

lmcs:1030 - Logical Methods in Computer Science, December 30, 2014, Volume 10, Issue 4 -
A Robust Class of Data Languages and an Application to LearningArticle

Authors: Benedikt Bollig ; Peter Habermehl ; Martin Leucker ORCID; Benjamin Monmege ORCID

    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.

    Volume: Volume 10, Issue 4
    Published on: December 30, 2014
    Imported on: January 29, 2014
    Keywords: Computer Science - Logic in Computer Science,Computer Science - Formal Languages and Automata Theory

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