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 …

Symbolic automata and transducers extend finite automata and transducers by allowing
transitions to carry predicates and functions over rich alphabet theories, such as linear …

Symbolic automata allow transitions to carry predicates over rich alphabet theories, such as
linear arithmetic, and therefore extend classic automata to operate over infinite alphabets …

We describe a large collection of benchmarks, publicly available through the wiki automata.
cs. ru. nl, of different types of state machine models: DFAs, Moore machines, Mealy …

Automata learning is a technique to infer an automaton model of a black-box system via
queries to the system. In recent years it has found widespread use both in industry and …

Symbolic automata (s-FAs) allow transitions to carry predicates over rich alphabet theories,
such as linear arithmetic, and therefore extend classic automata to operate over infinite …

Abstract Model learning is a black-box technique for constructing state machine models of
software and hardware components, which has been successfully used in areas such as …

We propose a generic categorical framework for learning unknown formal languages of
various types (eg finite or infinite words, weighted and nominal languages). Our approach is …

Parikh's Theorem is a fundamental result in automata theory with numerous applications in
computer science. These include software verification (eg infinite-state verification, string …

In this paper, we propose a novel algorithm to learn a Büchi automaton from a teacher who
knows an ω-regular language. The learned Büchi automaton can be a nondeterministic …