Consistent Identification in the Limit of Rigid Grammars from Strings Is NP-hard

In [Bus87] and [BP90] some 'discovery procedures' for classical categorial grammars were defined. These procedures take a set of structures (strings labeled with derivational information) as input and yield a set of hypotheses in the form of grammars.In [Kan98] learning functions based on these discovery procedures were studied, and it was shown that some of the classes associated with these functions can be identified in the limit (i.e. are learnable) from strings, by a computable function. The time complexity of these functions however was still left an open question.In this paper we will show that the learning functions for these learnable classes are all NP-hard.

[1]  Steffen Lange,et al.  Algorithmic Learning for Knowledge-Based Systems: Gosler Final Report , 1995 .

[2]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[3]  Dana Angluin,et al.  Inductive Inference of Formal Languages from Positive Data , 1980, Inf. Control..

[4]  Rolf Wiehagen,et al.  Learning and Consistency , 1995, GOSLER Final Report.

[5]  室 章治郎 Michael R.Garey/David S.Johnson 著, "COMPUTERS AND INTRACTABILITY A guide to the Theory of NP-Completeness", FREEMAN, A5判変形判, 338+xii, \5,217, 1979 , 1980 .

[6]  R. Stanley,et al.  Enumerative Combinatorics: Index , 1999 .

[7]  Mark A. Fulk Prudence and Other Conditions on Formal Language Learning , 1990, Inf. Comput..

[8]  Rolf Wiehagen,et al.  Ignoring data may be the only way to learn efficiently , 1994, J. Exp. Theor. Artif. Intell..

[9]  Werner Stein Consistent Polynominal Identification in the Limit , 1998, ALT.

[10]  Dana Angluin,et al.  Finding patterns common to a set of strings (Extended Abstract) , 1979, STOC.

[11]  Christophe Costa Florêcio On the Complexity of Consistent Identification of Some Classes of Structure Languages , 2000 .

[12]  Christophe Costa Florêncio On the Complexity of Consistent Identification of Some Classes of Structure Languages , 2000, ICGI.

[13]  Carl H. Smith,et al.  On the Complexity of Inductive Inference , 1986, Inf. Control..

[14]  Leonard Pitt,et al.  Inductive Inference, DFAs, and Computational Complexity , 1989, AII.

[15]  Makoto Kanazawa Learnable Classes of Categorial Grammars , 1998 .

[16]  Richard P. Stanley EXERCISES ON CATALAN AND RELATED NUMBERS , 1999 .

[17]  Gerald Penn,et al.  Categorial grammars determined from linguistic data by unification , 1990, Stud Logica.

[18]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[19]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[20]  Christophe Costa Florêncio Consistent Identification in the Limit of Any of the Classes k -Valued Is NP-hard , 2001, LACL.

[21]  Thomas Zeugmann,et al.  A Guided Tour Across the Boundaries of Learning Recursive Languages , 1995, GOSLER Final Report.

[22]  Daniel N. Osherson,et al.  Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists , 1990 .

[23]  G. Andrews ENUMERATIVE COMBINATORICS, VOLUME 2 (Cambridge Studies in Advanced Mathematics 62) By R ICHARD P. S TANLEY : 581 pp., £45.00 (US$69.95), ISBN 0 521 56069 1 (Cambridge University Press, 1999). , 2000 .