Learning Alternating Real-Time Automata

2026-06-18

AI Summary

This work addresses the high model complexity inherent in real-time temporal system modeling by proposing AL*RTA, the first provably terminating active learning algorithm for efficiently inferring Alternating Real-Time Temporal Automata (ARTA). ARTA are formally introduced herein, offering significantly enhanced model conciseness while preserving expressiveness equivalent to that of nondeterministic real-time temporal automata. AL*RTA integrates learning strategies from both alternating finite automata and real-time temporal automata, leveraging membership and equivalence queries to infer models. Experimental results demonstrate that, compared to NL*RTA, AL*RTA yields smaller and more compact models; although it requires slightly more queries, it substantially improves model interpretability and verification efficiency.

Abstract

We present the AL*RTA algorithm for learning alternating real-time automata (ARTAs) using membership and equivalence queries. AL*RTA combines ideas from AL*for learning alternating finite automata and NL*RTA for learning nondeterministic real-time automata. We first define ARTAs and show that alternation improves succinctness, although it does not increase expressive power. We then present AL*RTA and show its termination. Our empirical evaluation suggests that AL*RTA generally learns smaller automata than NL*RTA at the cost of more queries.

Problem

• alternating real-time automata
• automata learning
• real-time systems
• succinctness
• formal models

Innovation

• alternating real-time automata
• automata learning
• membership queries
• equivalence queries
• succinctness