A New Approach for Active Automata Learning Based on Apartness

We present L-#, a new and simple approach to active automata learning. Instead of focusing on equivalence of observations, like the L* algorithm and its descendants, L-# takes a different perspective: it tries to establish apartness, a constructive form of inequality. L-# does not require auxiliary notions such as observation tables or discrimination trees, but operates directly on tree-shaped automata. L-# has the same asymptotic query and symbol complexities as the best existing learning algorithms, but we show that adaptive distinguishing sequences can be naturally integrated to boost the performance of L-# in practice. Experiments with a prototype implementation, written in Rust, suggest that L-# is competitive with existing algorithms.