Abstract. Computability theorists have extensively studied sets whose elements can be enumerated by Turing machines. These sets, also called computably enumerable sets, can be identified with their Gödel codes. Although each Turing machine has a unique Gödel code, different Turing machines can enumerate the same set. Thus, knowing a computably enumerable set means knowing one of its infinitely many Gödel codes. In the approach to learning theory stemming from E.M. Gold's seminal paper [9], an inductive inference learner for a computably enumerable set A is a system or a device, usually algorithmic, which when successively (one by one) fed data for A outputs a sequence of Gödel codes (one by one) that at certain point stabilize at codes correct for A. The convergence is called semantic or behaviorally correct, unless the same code for A is eventually output, in which case it is also called syntactic or explanatory. There are classes of sets that are semantically inferable, but not syntactically inferable.Here, we are also concerned with generalizing inductive inference from sets, which are collections of distinct elements that are mutually independent, to mathematical structures in which various elements may be interrelated. This study was recently initiated by F. Stephan and Yu. 30