最小修正方法虽有局限，但在多数场景下仍能有效学习。
Who is Afraid of Minimal Revision?
- 采用最小信念修改策略，尽量保持原有信念不变
- 可学习有限可识别问题及正负数据下的有限假设
- 揭示了成功学习所需的先验可信度条件
信念修正理论中的最小改变原则要求在接纳新信息时，尽可能保持原有信念状态不变，这正是最小修正方法的核心。然而，与更激进的方法相比，最小修正在学习能力上存在不足：无法学习所有可学习的问题。本文首先表明，尽管如此，该方法在多种情境下仍具有效性：一是可学习所有有限可识别问题；二是可在正负数据条件下学习，前提为仅考虑有限可能性。随后，本文刻画了使最小修正能够成功学习的先验可信度分配形式，并对条件化与词典序升级也进行了相同分析。最后指出，当信息可能包含错误时，前述多数结论不再成立。
原文摘要 · Abstract (English)
The principle of minimal change in belief revision theory requires that, when accepting new information, one keeps one's belief state as close to the initial belief state as possible. This is precisely what the method known as minimal revision does. However, unlike less conservative belief revision methods, minimal revision falls short in learning power: It cannot learn everything that can be learned by other learning methods. We begin by showing that, despite this limitation, minimal revision is still a successful learning method in a wide range of situations. Firstly, it can learn any problem that is finitely identifiable. Secondly, it can learn with positive and negative data, as long as one considers finitely many possibilities. We then characterize the prior plausibility assignments (over finitely many possibilities) that enable one to learn via minimal revision, and do the same for conditioning and lexicographic upgrade. Finally, we show that not all of our results still hold when learning from possibly erroneous information.
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