Grammatical Inference, variously refered to as automata induction, grammar induction, and automatic language acquisition, refers to the process of learning of grammars and languages from data. Machine learning of grammars finds a variety of applications in syntactic pattern recognition, adaptive intelligent agents, diagnosis, computational biology, systems modelling, prediction, natural language acquisition, data mining and knowledge discovery.
Traditionally, grammatical inference has been studied by researchers in several research communities including: Information Theory, Formal Languages, Automata Theory, Language Acquisition, Computational Linguistics, Machine Learning, Pattern Recognition, Computational Learning Theory, Neural Networks, etc. Over the past few years, several conferences (e.g., the International Colloquium on Grammatical Inference, held thus far in 1993, 1994, 1996, 1998, 2000, and 2002) and workshops (such as the Workshop on Automata Induction, Grammatical Inference, and Knowledge Acquisition, held in conjunction with the International Conference on Machine Learning) have sought to bring together researchers working on grammatical inference in these areas. Some of this research is beginning to find significant applications in natural language interfaces to databases, bioinformatics, and related areas.
The grammatical inference community has begun to organize itself around its main conferences and workshops. A lot of information on grammatical inference and related areas is available on the world wide web. This homepage is designed to be a centralized resource information on Grammatical Inference and its applications. We hope that this information will be useful to both newcomers to the field as well as seasoned campaigners.