Prudence in vacillatory language identification

This paper settles a question about “prudent” “vacillatory” identification of languages. Consider a scenario in which an algorithmic deviceM is presented with all and only the elements of a languageL, andM conjectures a sequence, possibly infinite, of grammars. Three different criteria for success ofM onL have been extensively investigated in formal language learning theory. IfM converges to a single correct grammar forL, then the criterion of success is Gold's seminal notion ofTxtEx-identification. IfM converges to a finite number of correct grammars forL, then the criterion of success is calledTxtFex-identification. Further, ifM, after a finite number of incorrect guesses, outputs only correct grammars forL (possibly infinitely many distinct grammars), then the criterion of success is known asTxtBc-identification. A learning machine is said to beprudent according to a particular criterion of success just in case the only grammars it ever conjectures are for languages that it can learn according to that criterion. This notion was introduced by Osherson, Stob, and Weinstein with a view to investigating certain proposals for characterizing natural languages in linguistic theory. Fulk showed that prudence does not restrictTxtEx-identification, and later Kurtz and Royer showed that prudence does not restrictTxtBc-identification. This paper shows that prudence does not restrictTxtFex-identification.

[1]  Hartley Rogers,et al.  Gödel numberings of partial recursive functions , 1958, Journal of Symbolic Logic.

[2]  Manuel Blum,et al.  A Machine-Independent Theory of the Complexity of Recursive Functions , 1967, JACM.

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

[4]  D. C. Cooper,et al.  Theory of Recursive Functions and Effective Computability , 1969, The Mathematical Gazette.

[5]  Jr. Hartley Rogers Theory of Recursive Functions and Effective Computability , 1969 .

[6]  Manuel Blum,et al.  Toward a Mathematical Theory of Inductive Inference , 1975, Inf. Control..

[7]  Paul Young,et al.  An introduction to the general theory of algorithms , 1978 .

[8]  S. Pinker Formal models of language learning , 1979, Cognition.

[9]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[10]  Kenneth Wexler,et al.  Formal Principles of Language Acquisition , 1980 .

[11]  守屋 悦朗,et al.  J.E.Hopcroft, J.D. Ullman 著, "Introduction to Automata Theory, Languages, and Computation", Addison-Wesley, A5変形版, X+418, \6,670, 1979 , 1980 .

[12]  Klaus P. Jantke,et al.  Combining Postulates of Naturalness in Inductive Inference , 1981, J. Inf. Process. Cybern..

[13]  On extensional learnability , 1982, Cognition.

[14]  John Case,et al.  Machine Inductive Inference and Language Identification , 1982, ICALP.

[15]  Daniel N. Osherson,et al.  Learning Strategies , 1982, Inf. Control..

[16]  Daniel N. Osherson,et al.  Criteria of Language Learning , 1982, Inf. Control..

[17]  D. Osherson,et al.  A note on formal learning theory , 1982, Cognition.

[18]  D. Osherson,et al.  Learning theory and natural language , 1984, Cognition.

[19]  R. Treiman,et al.  Brown & Hanlon revisited: mothers' sensitivity to ungrammatical forms , 1984, Journal of Child Language.

[20]  C. Snow,et al.  Feedback to first language learners: the role of repetitions and clarification questions , 1986, Journal of Child Language.

[21]  Mark A. Fulk A study of inductive inference machines , 1986 .

[22]  S. Penner Parental responses to grammatical and ungrammatical child utterances. , 1987, Child development.

[23]  Stuart A. Kurtz,et al.  Prudence in language learning , 1988, COLT '88.

[24]  John Case The power of vacillation , 1988, COLT '88.

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

[26]  Daniel N. Osherson,et al.  On the study of first language acquisition , 1995 .

[27]  John Case,et al.  The Power of Vacillation in Language Learning , 1999, SIAM J. Comput..