当前位置: X-MOL 学术Artif. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A kinematics principle for iterated revision
Artificial Intelligence ( IF 14.4 ) Pub Date : 2022-11-17 , DOI: 10.1016/j.artint.2022.103827
Gabriele Kern-Isberner , Meliha Sezgin , Christoph Beierle

In probabilistic belief revision, the kinematics principle is a well-known and powerful principle which ensures that changing the probabilities of facts does not change unnecessarily conditional probabilities. A related principle, the principle of conditional preservation, has also been one of the main guidelines for the axioms of iterated belief revision in the seminal paper by Darwiche and Pearl. However, to date, a fully elaborated kinematics principle for iterated revision has not been presented. We aim to fill this gap in this paper by proposing a qualitative kinematics principle for iterated revision of epistemic states represented by total preorders. As new information, we allow sets of conditional beliefs, going far beyond the current state of the art of belief revision. We introduce a qualitative conditioning operator for total preorders which is compatible with conditioning for Spohn's ranking functions as far as possible, and transfer the technique of c-revisions to total preorders to provide a proof of concept for our kinematics principle at least for special revision scenarios.



中文翻译:

迭代修正的运动学原理

在概率信念修正中,运动学原理是一个众所周知且强大的原理,它确保改变事实的概率不会改变不必要的条件概率。一个相关的原则,条件保存原则,也是 Darwiche 和 Pearl 在开创性论文中迭代信念修正公理的主要指导原则之一。然而,迄今为止,尚未提出用于迭代修订的完全详尽的运动学原理。我们的目标是通过提出一个定性运动学原理来填补由总预购表示的认知状态的迭代修订的定性运动学原理。作为新信息,我们允许有条件的信念集,远远超出当前信念修正的艺术水平。

更新日期:2022-11-21
down
wechat
bug