NeuroCOLT

Neural Networks and Computational Learning Theory

 

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NeuroCOLT Technical Report NC-TR-95-030

Identifying Regular Languages over Partially-Commutative Monoids

Claudio Ferretti and Giancarlo Mauri
Università di Milano
Italy

Abstract
We define a new technique useful in identifying a subclass of regular languages defined on a free partially commutative monoid (regular trace languages), using equivalence and membership queries. Our algorithm extends an algorithm defined by Dana Angluin in 1987 to learn DFA's. The words of a trace language can be seen as equivalence classes of strings. We show how to extract, from a given equivalence class, a string of an unknown underlying regular langu age. These strings can drive the original learning algorithm which identify a regular string language that defines also the target trace language. In this way the algorithm applies also to classes of unrecognizable regular trace languages and, as a corollary, to a class of unrecognizable string languages. We also discuss bounds on the number of examples needed to identify the target language and on the time required to process them.

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