The three most common representations of states in iterated belief revision
are compared: explicit, by levels and by history. The first is a connected
preorder between models, the second is a list of formulae representing
equivalence classes, the third is the sequence of the previous revisions. The
latter depends on the revision semantics and on history rewriting, and the
latter depends on the allowed rewritings. All mechanisms represent all possible
states. A rewritten history of lexicographic revision is more efficient than
the other considered representations in terms of size with arbitrary history
rewritings. Establishing the redundancy of such a history is a mild rewriting.
It is coNP-complete in the general case, and is hard even on histories of two
revisions or revisions of arbitrary length of Horn formulae, and is polynomial
on histories of two Horn formulae. A minor technical result is a
polynomial-time algorithm for establishing whether a Horn formula is equivalent
to the negation of another Horn formula.

比较了置信修正中三种最常见的状态表示方式：显式表示、按级别表示和按历史记录表示的差异，并研究了它们之间的联系与区别。

我们是否能忘记学习过程？迭代信念修订中的状态表示

Can we forget how we learned? Representing states in iterated belief  revision}

Iterated Belief Change is the research area that investigates principles for
the dynamics of beliefs over (possibly unlimited) many subsequent belief
changes. In this paper, we demonstrate how iterated belief change is connected
to computation. In particular, we show that iterative belief revision is Turing
complete, even under the condition that broadly accepted principles like the
Darwiche-Pearl postulates for iterated revision hold.

本文介绍了关于信念动态演变的原则，重点探讨了迭代信念修订与计算之间的关系，并且证明了即使广泛接受的迭代修订原则如 Darwiche-Pearl 公理成立，迭代修订也是 Turing 完备的。