TY - GEN

T1 - Inductive inference of monogenic pure context-free languages

AU - Tanida, Noriyuki

AU - Yokomori, Takashi

PY - 1994/1/1

Y1 - 1994/1/1

N2 - This paper concerns a subclass of context-free languages, called pure context-free languages, which is generated by context-free grammar with only one type of symbol (i.e., terminals and nonterminals are not distinguished), and investigates the problem of identifying from positive data a restricted class of monogenic pure context-free languages (mono-PCF languages, in short). The class of mono-PCF languages is incomparable to the class of regular languages. We show that the class of mono-PCF languages is polynomial time identifiable from positive data. That is, there is an algorithm that, given a mono- PCF language L, identifies from positive data, a grammar generating L, called a monogenic pure context-free grammar (mono-PCF grammar) satisfying the property that the time for updating a conjecture is bounded by O(N3), where N is the sum of lengths of all positive data provided. This is in contrast with another result in this paper that the class of PCF languages is not identifiable in the limit from positive data.

AB - This paper concerns a subclass of context-free languages, called pure context-free languages, which is generated by context-free grammar with only one type of symbol (i.e., terminals and nonterminals are not distinguished), and investigates the problem of identifying from positive data a restricted class of monogenic pure context-free languages (mono-PCF languages, in short). The class of mono-PCF languages is incomparable to the class of regular languages. We show that the class of mono-PCF languages is polynomial time identifiable from positive data. That is, there is an algorithm that, given a mono- PCF language L, identifies from positive data, a grammar generating L, called a monogenic pure context-free grammar (mono-PCF grammar) satisfying the property that the time for updating a conjecture is bounded by O(N3), where N is the sum of lengths of all positive data provided. This is in contrast with another result in this paper that the class of PCF languages is not identifiable in the limit from positive data.

UR - http://www.scopus.com/inward/record.url?scp=22944461968&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=22944461968&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:22944461968

SN - 9783540585206

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 560

EP - 573

BT - Algorithmic Learning Theory - 4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994, Proceedings

A2 - Arikawa, Setsuo

A2 - Jantke, Klaus P.

PB - Springer Verlag

T2 - 4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994

Y2 - 10 October 1994 through 15 October 1994

ER -