The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas.
In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III, the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.
'The scope of the book, the detail of description, the uniformity of notation and treatment, and the enjoyable style make this book an important addition to the library of any computational linguist interested in language learning from data.' Machine Translation
'... this book raises important questions about learning grammars ...' Computing Reviews
"The book under review is the first textbook in this area, written by one of the renowned leading experts in grammatical inference."
Henning Fernau, Mathematical Reviews