No abstract

This paper summarises previous work on automatic families. It then investigates a natural size measure for members of an automatic family: the size of a member language in the family is defined as the length of its smallest index. This measure satisfies various properties similar to those of Kolmogorov complexity; in particular the size of a language depends only up to a constant on the underlying automatic family. This family of size measures is extended to a measure on all regular sets. This extension is given by the maximum number of states visited in some run of the minimal deterministic finite automaton recognising the language. Furthermore, a characterisation is given regarding when a class of languages is a subclass of an automatic family.

Automatic classes are classes of languages for which a finite automaton can decide the membership problem for the languages in the class, in a uniform way, given an index for the language. For alphabet size of at least 4, every automatic class of erasing pattern languages is contained, for some constant n, in the class of all languages generated by patterns which contain (1) every variable only once and (2) at most n symbols after the first occurrence of a variable. It is shown that such a class is automatically learnable using a learner with the length of the long-term memory being bounded by the length of the first example seen. The study is extended to show the learnability of related classes such as the class of unions of two pattern languages of the above type.

No abstract

Abstract. Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long term memory, depending on the input datum, via an automatic function, that is, via a function whose graph is recognised by a finite automaton. Many variants of automatic learners are investigated: where the long term memory is restricted to be the just prior hypothesis whenever this exists, cannot be of size larger than the size of the longest example or has to consist of a constant number of examples seen so far. Furthermore, learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant. These models are generalisations of the model of feedback queries, given by Lange, Wiehagen and Zeugmann.