Learning regular sets from queries and counterexamples
Information and Computation
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
On the learnability of infinitary regular sets
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
A geometric hierarchy beyond context-free languages
Theoretical Computer Science
wMSO theories as grammar formalisms
Theoretical Computer Science - Algebraic methods in language processing
Query Learning of Regular Tree Languages: How to Avoid Dead States
Theory of Computing Systems
Learning a regular tree language from a teacher
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Two Equivalent Regularizations for Tree Adjoining Grammars
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Generalizing over several learning settings
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Theoretical Computer Science
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We generalize a learning algorithm by Drewes and Högberg [1] for regular tree languages based on a learning model proposed by Angluin [2] to recognizable tree languages of arbitrarily many dimensions, so-called multi-dimensional trees. Trees over multi-dimensional tree domains have been defined by Rogers [3,4]. However, since the algorithm by Drewes and Högberg relies on classical finite state automata, these structures have to be represented in another form to make them a suitable input for the algorithm: We give a new representation for multi-dimensional trees which establishes them as a direct generalization of classical trees over a partitioned alphabet, and show that with this notation Drewes' and Högberg's algorithm is able to learn tree languages of arbitrarily many dimensions. Via the correspondence between trees and string languages ("yield operation") this is equivalent to the statement that this way even some string language classes beyond context-freeness have become learnable with respect to Angluin's learning model as well.