Results of the PAutomaC Probabilistic Automaton Learning Competition
Sicco Verwer, Rémi Eyraud and Colin de la Higuera
JMLR Workshop and Conference Proceedings
Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. The purpose of this paper is to provide an overview of the implementation details of PAutomaC and to report the final results of the competition.