CONCISE REPRESENTATIONS OF REGULAR LANGUAGES BY DEGREE AND PROBABILISTIC FINITE AUTOMATA

Meyer and Fischer [MF] proved that nondeterministic finite automata (NFA) can be exponentially more concise than deterministic finite automata (DFA) in their representations of regular languages. Several variants of that basic finite state machine model are now being used to analyze parallelism and to build real-time software systems [HL+]. Even though these variants can sometimes represent regular languages in a more concise manner than NFA, the underlying models fundamentally differ from NFA in how they operate. Degree automata [W] (DA), however, differ from NFA only in their acceptance criteria and accept only regular languages. We show here that DA are also exponentially more concise than NFA on some sequences of regular languages. We also show that the conciseness of probabilistic automata [R] with isolated cutpoints can be unbounded over DA and, concurrently, i.e., over the same sequence of languages, those DA can be exponentially more concise than NFA.