# A Robust Class of Data Languages and an Application to Learning

arXiv: https://arxiv.org/abs/1411.6646 · HTML: https://mlsanity.com/paper/1411.6646/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [Nominal Topology for Data Languages](https://mlsanity.com/paper/2304.13337/) (2023)
- [Inference of Deterministic Finite Automata via Q-Learning](https://mlsanity.com/paper/2510.17386/) (2025)

## Abstract (arXiv)

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.