Greybox Learning of Languages Recognizable by Event-Recording Automata

TL;DR

This paper introduces a greybox learning approach for efficiently learning event-recording automata that recognize timed languages, avoiding complex region automaton construction and demonstrating effectiveness through examples.

Contribution

The paper presents a novel greybox learning framework for timed automata that reduces complexity by bypassing region automaton construction, with practical implementation and heuristics.

Findings

Effective learning of automata with minimal control states

Avoids complex region automaton construction

Demonstrated success on multiple examples

Abstract

In this paper, we revisit the active learning of timed languages recognizable by event-recording automata. Our framework employs a method known as greybox learning, which enables the learning of event-recording automata with a minimal number of control states. This approach avoids learning the region automaton associated with the language, contrasting with existing methods. We have implemented our greybox learning algorithm with various heuristics to maintain low computational complexity. The efficacy of our approach is demonstrated through several examples.