Feasible Iteration of Feasible Learning Functionals

Abstract

For learning functions in the limit, an algorithmic learner obtains successively more data about a function and calculates trials each resulting in the output of a corresponding program, where, hopefully, these programs eventually converge to a correct program for the function. The authors desired to provide a feasible version of this learning in the limit — a version where each trial was conducted feasibly and there was some feasible limit on the number of trials allowed. Employed were basic feasible functionals which query an input function as to its values and which provide each trial. An additional tally argument 0ⁱ was provided to the functionals for their execution of the i-th trial. In this way more time resource was available for each successive trial. The mechanism employed to feasibly limit the number of trials was to feasibly count them down from some feasible notation for a constructive ordinal. Since all processes were feasible, their termination was feasibly detectable, and, so, it was possible to wait for the trials to terminate and suppress all the output programs but the last. Hence, although there is still an iteration of trials, the learning was a special case of what has long been known as total Fin-learning, i.e., learning in the limit, where, on each function, the learner always outputs exactly one conjectured program. Our general main results provide for strict learning hierarchies where the trial count down involves all and only notations for infinite limit ordinals. For our hierarchies featuring finitely many limit ordinal jumps, we have upper and lower total run time bounds of our feasible Fin-learners in terms of finite stacks of exponentials. We provide, though, an example of how to regain feasibility by a suitable parameterized complexity analysis.

Case and Paddock were supported in part by NSF grant number NSF CCR-0208616. We are also grateful to anonymous referees for many helpful suggestions. One such referee provided hints about the truth and truth and proof, respectively, of what became, then, Lemmas 6 and 7; hence, these results are joint work with that referee. This same referee suggested, for the future, team learning as an approach to studying some probabilistic variants of our learning criteria.