In the field of grammatical inference most of the existing learning models have been inspired by the process of human language acquisition. For example, the query learning model is a representation of our intuition that children acquire their native language while interacting with their environment. Apart from the well-established membership and equivalence queries, many other types of queries have been introduced. Nevertheless, none of these reflect an important aspect of the way children learn languages, namely that although they are not explicitly provided negative examples (i. e. , words that are not in the language or ungrammatical sentences), adults correct them when they make mistakes. In this book we thoroughly investigate a recently introduced type of query, called CORRECTION QUERY, which copes with this particularity of children language acquisition. Here are some of the topics addressed: What is the power of correction queries? Can we compare them with other query learning or Gold-style learning models? Which of them provides the learner with more information? Can we build efficient algorithms that learn well-known classes of languages with correction queries?
Cristina Tîrn uc was born in Bac u, Romania on July the 2nd
1979. In 2003 she got her BS in mathematics and computer science
at the University of Bucharest and her BEc at the Academy of
Economic Studies, Bucharest. In 2009 she obtains the PhD degree
in computer science and artificial intelligence at Rovira i
Virgili University, Tarragona, Spain.
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