Learning Symbolic Timed Models from Concrete Timed Data.

We present a technique for learning explainable timed automata from passive observations of a black-box function, such as an artificial intelligence system. Our method accepts a single, long, timed word with mixed input and output actions and learns a Mealy machine with one timer. The primary advantage of our approach is that it constructs a symbolic observation tree from a concrete timed word. This symbolic tree is then transformed into a human comprehensible automaton. We provide a prototype implementation and evaluate it by learning the controllers of two systems: a brick-sorter conveyor belt trained with reinforcement learning and a real-world derived smart traffic light controller. We compare different model generators using our symbolic observation tree as their input and achieve the best results using k -tails. In our experiments, we learn smaller and simpler automata than existing passive timed learners while maintaining accuracy.