归纳推理中基于（语义）目击学习者的范式
Normal Forms for (Semantically) Witness-Based Learners in Inductive Inference
摘要：
我们研究学习者（可计算设备）推断形式语言，一种被称为极限语言学习或归纳推理的设置。特别是，我们要求我们调查的学习者是基于成功的，也就是说，证明他们的每一个想法的变化。除了作为一个学习任务的自然要求，这一限制值得特别注意，因为它是各种重要的学习范式的特殊化。特别是，在基于成功的学习的帮助下，解释性学习者在这些看似无与伦比的范式下表现出同样强大的能力。尽管如此，到目前为止，基于成功的学习者只被稀疏地研究。在这项工作中，我们进行了深入的研究，这些学习者时，需要句法和语义收敛，并获得其范式。在前一种情况下，我们扩展了已知的结果，使它们包括基于成功的学习，并将其推广到各种学习者。过渡到行为正确的学习，我们还提供了范式为语义的学习者基于。最值得注意的是，我们表明，集驱动的全球语义的成功为基础的学习者同样强大的黄金风格的语义保守的对应。这样的结果是理解的关键，但未被发现的，各种重要的学习范式之间的相互关系时，正确的行为学习。
英文摘要：
We study learners (computable devices) inferring formal languages, a setting referred to as language learning in the limit or inductive inference. In particular, we require the learners we investigate to be witness-based, that is, to justify each of their mind changes. Besides being a natural requirement for a learning task, this restriction deserves special attention as it is a specialization of various important learning paradigms. In particular, with the help of witness-based learning, explanatory learners are shown to be equally powerful under these seemingly incomparable paradigms. Nonetheless, until now, witness-based learners have only been studied sparsely. In this work, we conduct a thorough study of these learners both when requiring syntactic and semantic convergence and obtain normal forms thereof. In the former setting, we extend known results such that they include witness-based learning and generalize these to hold for a variety of learners. Transitioning to behaviourally correct learning, we also provide normal forms for semantically witness-based learners. Most notably, we show that set-driven globally semantically witness-based learners are equally powerful as their Gold-style semantically conservative counterpart. Such results are key to understanding the, yet undiscovered, mutual relation between various important learning paradigms when learning behaviourally correctly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
