Hypothesis Formation and Language Acquisition with an Infinitely-Often Correct Teacher

The presence of an "infinitely-often correct teacher" in scientific inference and language acquisition is motivated and studied. The treatment is abstract. In the practice of science, a scientist performs experiments to gather experimental data about some phenomenon, and then tries to construct an explanation (or theory) for the phenomenon. A model for the practice of science is an inductive inference machine (a scientist) learning a program (an explanation) from the graph (set of experiments) of a recursive function (phenomenon). It is argued that this model of science is not an adequate one as scientists, in addition to performing experiments, make use of some approximate explanation (based on the "state of the art") about the phenomenon under investigation. An attempt has been made to model this approximate explanation as an additional information in the scientific process. It is shown that inference power of machines is improved in the presence of an approximate explanation. The quality of this approximate information is modeled using certain "density" notions. It is shown that additional information about a "better" quality approximate explanation enhances the inference power of learning machines as scientists more than a "not so good" approximate explanation. Inadequacies in Gold's paradigm of language learning are investigated. It is argued that Gold's model fails to incorporate any additional information that children get from their environment. Children are sometimes told about some grammatical rule that enumerates elements of the language. These rules are some sort of additional information. Also, children are being given some information about what is not in the language. Sometimes, they are rebuked for making incorrect utterances or are told of a rule that enumerates certain non-elements of the language. An attempt has been made to extend Gold's model to incorporate both these kinds of additional information. It is shown that either type of additional information enhances the learning power of formal language learning devices.

[1]  Rolf Wiehagen,et al.  Research in the theory of inductive inference by GDR mathematicians - A survey , 1980, Inf. Sci..

[2]  Keh-Jiann Chen,et al.  Tradeoffs in machine inductive inference , 1981 .

[3]  G. L. Collected Papers , 1912, Nature.

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

[5]  Rolf Wiehagen,et al.  Identification of Formal Languages , 1977, MFCS.

[6]  P. Dale Language development; structure and function , 1972 .

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

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

[9]  Dana Angluin,et al.  Finding Patterns Common to a Set of Strings , 1980, J. Comput. Syst. Sci..

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

[11]  Roger S. Brown,et al.  Three Processes in the Child's Acquisition of Syntax , 1964 .

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

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

[14]  Rolf Wiehagen,et al.  Inductive Inference with Additional Information , 1979, J. Inf. Process. Cybern..

[15]  Carl H. Smith,et al.  Inductive Inference: Theory and Methods , 1983, CSUR.

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

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

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

[19]  John Case,et al.  Comparison of Identification Criteria for Machine Inductive Inference , 1983, Theor. Comput. Sci..

[20]  Rolf Wiehagen,et al.  Characterization Problems in the Theory of Inductive Inference , 1978, ICALP.

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

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

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

[24]  James S. Royer,et al.  Inductive Inference of Approximations , 1986, Inf. Control..