Effectivity questions for Kleene's recursion theorem

Abstract The present paper investigates the quality of numberings measured in three different ways: (a) the complexity of finding witnesses of Kleene's Recursion Theorem in the numbering; (b) for which learning notions from inductive inference the numbering is an optimal hypothesis space; (c) the complexity needed to translate the indices of other numberings to those of the given one. In all three cases, one assumes that the corresponding witnesses or correct hypotheses are found in the limit and one measures the complexity with respect to the best criterion of convergence which can be achieved. The convergence criteria considered are those of finite, explanatory, vacillatory and behaviourally correct convergence. The main finding is that the complexity of finding witnesses for Kleene's Recursion Theorem and the optimality for learning are independent of each other. Furthermore, if the numbering is optimal for explanatory learning and also allows to solve Kleene's Recursion Theorem with respect to explanatory convergence, then it also allows to translate indices of other numberings with respect to explanatory convergence.

[1]  John Case,et al.  Program Self-Reference in Constructive Scott Subdomains , 2009, Theory of Computing Systems.

[2]  James S. Royer A Connotational Theory of Program Structure , 1987, Lecture Notes in Computer Science.

[3]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

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

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

[6]  R. Soare Recursively enumerable sets and degrees , 1987 .

[7]  Sanjay Jain,et al.  Numberings optimal for learning , 2010, J. Comput. Syst. Sci..

[8]  John Case,et al.  Control structures in hypothesis spaces: the influence on learning , 2002, Theor. Comput. Sci..

[9]  John Case,et al.  Effectivity Questions for Kleene's Recursion Theorem , 2013, LFCS.

[10]  Stephen Cole Kleene,et al.  On notation for ordinal numbers , 1938, Journal of Symbolic Logic.

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

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

[13]  Klaus Ambos-Spies,et al.  Inductive inference and computable numberings , 2011, Theor. Comput. Sci..

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

[15]  Arun Sharma,et al.  Characterizing Language Identification by Standardizing Operations , 1994, J. Comput. Syst. Sci..

[16]  Rodney G. Downey,et al.  Algorithmic Randomness and Complexity , 2010, Theory and Applications of Computability.

[17]  Jason Teutsch,et al.  An incomplete set of shortest descriptions , 2012, The Journal of Symbolic Logic.

[18]  Arun Sharma,et al.  Characterizing Language Identification in Terms of Computable Numberings , 1997, Ann. Pure Appl. Log..

[19]  John Case,et al.  Periodicity in generations of automata , 1974, Mathematical systems theory.

[20]  Samuel E. Moelius,et al.  Program self-reference , 2009 .

[21]  Yuri L. Ershov,et al.  Theory of Numberings , 1999, Handbook of Computability Theory.

[22]  A. Nies Computability and randomness , 2009 .

[23]  Susumu Hayashi Mathematics based on incremental learning - Excluded middle and inductive inference , 2006, Theor. Comput. Sci..

[24]  Sanjay Jain,et al.  Index sets and universal numberings , 2011, J. Comput. Syst. Sci..

[25]  Sanjay Jain Hypothesis spaces for learning , 2011, Inf. Comput..

[26]  P. Odifreddi Classical recursion theory , 1989 .

[27]  S. C. Kleene,et al.  Introduction to Metamathematics , 1952 .

[28]  Gregory A. Riccardi The Independence of Control Structures in Abstract Programming Systems , 1981, J. Comput. Syst. Sci..

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

[30]  Cristian S. Calude Information and Randomness: An Algorithmic Perspective , 1994 .

[31]  Andrea Sorbi,et al.  Completeness and Universality of Arithmetical Numberings , 2003 .