@TECHREPORT{Cer14techrep,
  author = {Peter Černo},
  title = {Grammatical Inference of Lambda-Confluent Context Rewriting Systems},
  institution = {Department of Software and Computer Science Education, Faculty of
    Mathematics and Physics, Charles University},
  year = {2015},
  type = {technical report},
  number = {2015/1/KSVI},
  address = {Prague},
  note = {28 pages},
  timestamp = {2015.01.25}
  abstract = {Although a lot of methods have been proposed to learn regular languages, learning more complex language classes is still a challenging task. One attractive approach is to consider non-classical formalisms that provide alternative ways of representing interesting classes of languages and give rise to new promising learning techniques. To this end we use the so-called context rewriting systems, which are defined as string-rewriting systems extended by contexts, and propose a novel learning algorithm for inferring various restricted classes of $\lambda$-confluent context rewriting systems. We show that, under certain conditions, it is possible to identify in polynomial time (but not polynomial data) any target $\lambda$-confluent context rewriting system with minimal width of instructions from informant. We discuss the complexity of the learning algorithm, relate the considered language classes to the Chomsky hierarchy and finally raise some open questions and further research directions.}
}