Abstract
This entry formalizes some classical concepts and results
from inductive inference of recursive functions. In the basic setting
a partial recursive function ("strategy") must identify
("learn") all functions from a set ("class") of
recursive functions. To that end the strategy receives more and more
values
Other types of
inference considered are finite learning (FIN), behaviorally correct
learning in the limit (BC), and some variants of LIM with restrictions
on the hypotheses: total learning (TOTAL), consistent learning (CONS),
and class-preserving learning (CP). The main results formalized are
the proper inclusions
The above is based on a formalization of recursive functions heavily inspired by the Universal Turing Machine entry by Xu et al., but different in that it models partial functions with codomain nat option. The formalization contains a construction of a universal partial recursive function, without resorting to Turing machines, introduces decidability and recursive enumerability, and proves some standard results: existence of a Kleene normal form, the s-m-n theorem, Rice's theorem, and assorted fixed-point theorems (recursion theorems) by Kleene, Rogers, and Smullyan.
License
Topics
Session Inductive_Inference
- Partial_Recursive
- Universal
- Standard_Results
- Inductive_Inference_Basics
- CP_FIN_NUM
- CONS_LIM
- Lemma_R
- LIM_BC
- TOTAL_CONS
- R1_BC
- Union