This paper presents a new approach for synthesizing transformations on tree-structured
data, such as Unix directories and XML documents. We consider a general abstraction for …

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 …

We address the issue of efficiently automating assume-guarantee reasoning for simulation
conformance between finite state systems and specifications. We focus on a non-circular …

In this paper, we give an overview on some algorithms for learning automata. Starting with
Biermann's and Angluin's algorithms, we describe some of the extensions catering for …

We develop new algorithms for learning monadic node selection queries in unranked trees
from annotated examples, and apply them to visually interactive Web information extraction …

Automata learning is a technique that has successfully been applied in verification, with the
automaton type varying depending on the application domain. Adaptations of automata …

Standard models of language learning are concerned with weak learning: the learner,
receiving as input only information about the strings in the language, must learn to …

We describe the first approach to automatically repair bugs in context-free grammars: given
a grammar that fails some tests in a given test suite, we iteratively and gradually transform …

We generalize an inference algorithm by Angluin, that learns a regular string language from
a" minimally adequate teacher", to regular tree languages. The (deterministic bottom-up) …

We denote by qa−−→ q that (q, a, q)∈→. The main differences between a labelled transition
system and a finite-state automaton are that the set of states Q and the alphabet L (and …