提出一种新方法，让信念收缩能同时处理多个信息删除，并支持多次连续操作。
Parallel Belief Contraction via Order Aggregation
- 用扩展的队列聚合机制实现多条信息并行删除
- 支持多次连续信念更新，保持逻辑一致性
- 适合需要系统化处理信息删除的逻辑与认知研究
标准的‘串行’（又称‘单例’）信念收缩模型描述了个体信念体系在移除单一信息时的反应。现有研究对‘并行’（又称‘打包’或‘多重’）变化的扩展主要集中在单步并行收缩：即一次移除多个信息后信念的变化行为。此外，这些研究大多仅将串行收缩操作的弱性质推广到并行情形。本文则关注具有更强性质的串行收缩操作的并行扩展，并进一步探讨迭代情形——即一系列并行收缩后的信念演化。为此，我们提出一种通用方法，将串行迭代信念变化算子扩展至并行场景，基于对Booth & Chandler提出的团队队列（TeamQueue）二元序聚合器的n元推广。
原文摘要 · Abstract (English)
The standard ``serial'' (aka ``singleton'') model of belief contraction models the manner in which an agent's corpus of beliefs responds to the removal of a single item of information. One salient extension of this model introduces the idea of ``parallel'' (aka ``package'' or ``multiple'') change, in which an entire set of items of information are simultaneously removed. Existing research on the latter has largely focussed on single-step parallel contraction: understanding the behaviour of beliefs after a single parallel contraction. It has also focussed on generalisations to the parallel case of serial contraction operations whose characteristic properties are extremely weak. Here we consider how to extend serial contraction operations that obey stronger properties. Potentially more importantly, we also consider the iterated case: the behaviour of beliefs after a sequence of parallel contractions. We propose a general method for extending serial iterated belief change operators to handle parallel change based on an n-ary generalisation of Booth & Chandler's TeamQueue binary order aggregators.
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