Learning Alternating Real-Time Automata

We present the ALRTA algorithm for learning alternating real-time automata (ARTAs) using membership and equivalence queries. ALRTA combines ideas from ALfor learning alternating finite automata and NLRTA 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 ALRTA and show its termination. Our empirical evaluation suggests that ALRTA generally learns smaller automata than NL*RTA at the cost of more queries.