Learning Event-recording Automata Passively
Abstract
This paper presents a state-merging algorithm for learning timed languages definable by Event-Recording Automata (ERA) using positive and negative samples in the form of symbolic timed words. Our algorithm, LEAP (Learning Event-recording Automata Passively), constructs a possibly nondeterministic ERA from such samples based on merging techniques. We prove that determining whether two ERA states can be merged while preserving sample consistency is an NP-complete problem, and address this with a practical SMT-based solution. Our implementation demonstrates the algorithm's effectiveness through examples. We also show that every ERA-definable language can be inferred using our algorithm with a suitable sample.
Cite
@article{arxiv.2508.03627,
title = {Learning Event-recording Automata Passively},
author = {Anirban Majumdar and Sayan Mukherjee and Jean-François Raskin},
journal= {arXiv preprint arXiv:2508.03627},
year = {2025}
}
Comments
Shorter version of this article has been accepted at ATVA 2025