Communications of the ACM
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Algorithmic Program DeBugging
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Machine Learning
Machine Learning
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Relations between gold-style learning and query learning
Information and Computation
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Discontinuities in pattern inference
Theoretical Computer Science
One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
A Note on the Relationship between Different Types of Correction Queries
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
One-shot learners using negative counterexamples and nearest positive examples
Theoretical Computer Science
Necessary and sufficient conditions for learning with correction queries
Theoretical Computer Science
Relations between Gold-style learning and query learning
Information and Computation
A characterization of the language classes learnable with correction queries
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
Robust learning of automatic classes of languages
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Four one-shot learners for regular tree languages and their polynomial characterizability
Theoretical Computer Science
Robust learning of automatic classes of languages
Journal of Computer and System Sciences
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A natural approach towards powerful machine learning systems is to enable options for additional machine/user interactions, for instance by allowing the system to ask queries about the concept to be learned. This motivates the development and analysis of adequate formal learning models. In the present paper, we investigate two different types of query learning models in the context of learning indexable classes of recursive languages: Angluin's original model and a relaxation thereof, called learning with extra queries. In the original model the learner is restricted to query languages belonging to the target class, while in the new model it is allowed to query other languages, too. As usual, the following standard types of queries are considered: superset, subset, equivalence, and membership queries. The learning capabilities of the resulting query learning models are compared to one another and to different versions of Gold-style language learning from only positive data and from positive and negative data (including finite learning, conservative inference, and learning in the limit). A complete picture of the relation of all these models has been elaborated. A couple of interesting differences and similarities between query learning and Gold-style learning have been observed. In particular, query learning with extra superset queries coincides with conservative inference from only positive data. This result documents the naturalness of the new query model.