No abstract

No abstract

The problem of learning a minimal consistent model from a set of labeled sequences of symbols is addressed from a satisfiability modulo theories perspective. We present two encodings for deterministic finite automata and extend one of these for Moore and Mealy machines. Our experimental results show that these encodings improve upon the state-of-the-art, and are useful in practice for learning small models.

A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related -yet seemingly novel -problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers.

Abstract. We study a general class of distance metrics for deterministic Mealy machines. The metrics are induced by weight functions that specify the relative importance of input sequences. By choosing an appropriate weight function we may fine-tune our metric so that it captures some intuitive notion of quality. In particular, we present a metric that is based on the minimal number of inputs that must be provided to obtain a counterexample, starting from states that can be reached by a given set of logs. For any weight function, we may boost the performance of existing model learning algorithms by introducing an extra component, which we call the Comparator. Preliminary experiments show that use of the Comparator yields a significant reduction of the number of inputs required to learn correct models, compared to current state-of-the-art algorithms. In existing automata learning algorithms, the quality of subsequent hypotheses may decrease. Generalizing a result of Smetsers et al.,we show that the quality of hypotheses that are generated by the Comparator never decreases.