Learning definite Horn formulas from closure queries

Abstract: A definite Horn theory is a set of n-dimensional Boolean vectors whose characteristic function is expressible as a definite Horn formula, that is, as conjunction of definite Horn clauses. The class of definite Horn theories is known to be learnable under different query learning settings, such as learning from membership and equivalence queries or learning from entailment. We propose yet a different type of query: the closure query. Closure queries are a natural extension of membership queries and also a variant, appropriate in the context of definite Horn formulas, of the so-called correction queries. We present an algorithm that learns conjunctions of definite Horn clauses in polynomial time, using closure and equivalence queries, and show how it relates to the canonical Guigues–Duquenne basis for implicational systems. We also show how the different query models mentioned relate to each other by either showing full-fledged reductions by means of query simulation (where possible), or by showing their connections in the context of particular algorithms that use them for learning definite Horn formulas.

 Fuente: Theoretical Computer Science, Volume 658, Part B, 7 January 2017, Pages 346–356

Editorial: Elsevier

 Fecha de publicación: 01/01/2017

Nº de páginas: 11

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.tcs.2015.12.019

ISSN: 0304-3975,1879-2294

Proyecto español: TIN2011-27479-C04-04 ; MTM2014-55262-P ; 2014SGR 890

Url de la publicación: https://doi.org/10.1016/j.tcs.2015.12.019