Identification of biRFSA languages

International audienceThe task of identifying a language from a set of its words is not an easy one. For instance, it is not feasible to identify regular languages in the general case. Therefore, looking for subclasses of regular languages that can be identi?ed in this framework is an interesting problem. One of the most classical identi?able classes is the class of reversible languages, introduced by D. Angluin, also called bideterministic languages as they can be represented by deterministic automata (DFA) whose reverse is also deterministic. Residual Finite State Automata (RFSA) on the other hand is a class of non deterministic automata that shares some properties with DFA. In particular, DFA are RFSA and RFSA can be much smaller. We study here learnability of the class of languages that can be represented by biRFSA: RFSA whose reverse are RFSA. We prove that this class is not identi?able in general but we present two subclasses that are learnable, the second one being identi?able in polynomial time

Inferring descriptive generalisations of formal languages

In the present paper, we introduce a variant of Gold-style learners that is not required to infer precise descriptions of the languages in a class, but that must nd descriptive patterns, i. e., optimal generalisations within a class of pattern languages. Our rst main result characterises those indexed families of recursive languages that can be inferred by such learners, and we demonstrate that this characterisation shows enlightening connections to Angluin's corresponding result for exact inference. Furthermore, this result reveals that our model can be interpreted as an instance of a natural extension of Gold's model of language identi cation in the limit. Using a notion of descriptiveness that is restricted to the natural subclass of terminal-free E-pattern languages, we introduce a generic inference strategy, and our second main result characterises those classes of languages that can be generalised by this strategy. This characterisation demonstrates that there are major classes of languages that can be generalised in our model, but not be inferred by a normal Gold-style learner. Our corresponding technical considerations lead to insights of intrinsic interest into combinatorial and algorithmic properties of pattern languages