We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounded memory states (BMS) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states. We give the complete map of all pairwise relations for an established collection of criteria of successfull learning. Most prominently, we show that non-U-shapedness is not restrictive, while conservativeness and (strong) monotonicity are. Some results carry over from iterative learning by a general lemma showing that, for a wealth of restrictions (the semantic restrictions), iterative and bounded memory states learning are equivalent. We also give an example of a non-semantic restriction (strongly non-U-shapedness) where the two settings differ.
翻译:我们调查用感官推理机从文字中学习语言的藏书,该机可以进入当前的数据目录和以状态的形式的封闭式记忆。这种受约束的记忆状态(BMS)学习者被认为成功,如果最终在正确的假设上定下来,而只是利用有限的许多不同的状态。我们给出了所有对称关系的完整地图,以收集成功的学习标准。最突出的是,我们显示非U形不是限制性的,而保守性和(强的)单调性。一些结果由普通的Lemma的迭代学习产生,表明对于许多限制(语义限制)来说,迭接和受约束的记忆状态是相等的。我们还举了两种环境不同的非隔离性限制(强烈的非U形性)的例子。