Advances in Learning Formal Languages

we present an overview in the advances related to the learning of formal languages i.e. development in the grammatical inference research. The problem of learning correct grammars for the unknown languages is known as grammatical inference. It is considered a main subject of inductive inference, and grammars are important representations to be investigated in machine learning from both theoretical and practical points of view. Application area of grammatical inference is increasing day by day, and it is still required to find a task where grammatical inference models have done much better than other machine learning or pattern recognition programs. However, it is known that making research in this area is a computationally hard problem. This paper mainly explores the area, its applications, various learning paradigms, the case of context-free grammars, challenges, recent trends etc., and cites the important literature on these.