Joint acquisition of a forecast and a library [ftip-00NM]
Joint acquisition of a forecast and a library [ftip-00NM]
A developmental learner retains a predictive state \(b_t\) and a library \(L_t\) of representations, subroutines or reusable reasoning procedures. From the observed history it updates its forecast of future tasks and decides whether to extend, refine or discard library elements. The forecast need not be an explicit Bayesian posterior, and the library may be encoded in parameters. The defining feature is that the learner uses anticipated downstream utility in deciding what to acquire.
The two learning problems interact. A new representation can make a previously obscure regularity in the task process easier to recognize; a revised forecast can change which representation is worth learning. Consequently, a predictor operating over a fixed, human-supplied list of perfect abstractions removes part of the proposed acquisition problem. A bounded construction should describe how candidate structure is generated, how its consequences are grounded, and how experience updates both prediction and selection. Initial program libraries, pretrained predictors and demonstration histories remain disclosed endowments.
One possible developmental procedure generates candidate decompositions from earlier solved tasks, predicts their usefulness over a finite future horizon, and spends a capped amount of work testing the most promising candidates on independently sampled continuations. Failed predictions supply feedback. This is an explicit algorithmic pattern, not a free forecast oracle: generating continuations, constructing a simulator, evaluating candidates and revising the predictor all cost work. With public computable laws, the closed campaign may implement this procedure too. The intended advantage must come from the cost of acquiring a sufficiently useful procedure from the specified endowments.
A developed contributor can teach the recipient its selection method, transmit a grounded library with its conditions of use, or help initialize a predictor. The recipient must acquire and apply the contribution within the assistance cap. Later library selection and task solving are performed without further uncharged source access. A one-time selection for a revealed history is a weaker form of help than learning a reusable prospective policy; transfer to fresh episodes distinguishes them. Neither form requires matching the source's internal concepts.