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?
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