我们能忘记自己是如何学习的吗？迭代信念修正中的信念冗余
Can we forget how we learned? Doxastic redundancy in iterated belief revision
AI总结：
本文研究迭代信念修正中的信念冗余问题，提出一种算法检查遗忘信念获取过程是否导致信息丢失，并分析了其在多种修正算子下的计算复杂度。
摘要：
遗忘某个信念获取过程可能不会因为其他过程而导致信息丢失。检查是否丢失并不简单，因为每次信念修正的贡献并非与其他修正隔离，且相同信息可能不是直接给出而是通过推导得出。本文针对多种迭代信念修正算子（包括词典式、自然、严重、纯严重、中度严重、受限、极度激进和完全交汇修正）提出了一种检查遗忘是否减少信息的算法。在最坏情况下，该算法可能需要指数级时间，考虑到该问题是coNP-hard的，即使在Horn限制下也是如此，这一结果是意料之中的。对于词典式修正的同质序列，该问题属于coNP。
英文摘要：
Forgetting a belief acquisition episode may not cause information loss because of the others. Checking whether it does is not obvious, as the contribution of each belief revision is not isolated from the others, and the same information may be given not directly but by deduction. An algorithm for checking whether forgetting reduces information is given for a number of iterated belief revision operators: lexicographic, natural, severe, plain severe, moderate severe, restrained, very radical and full meet revisions. It may take exponential time in the worst case, which is expected given that the problem is coNP-hard, even in the Horn restriction. It is in coNP for homogeneous sequences of lexicographic revisions.
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。
