提出一种统一方法，让信念更新能迭代并行处理。
Parallel Belief Revision via Order Aggregation
- 用团队队列聚合器扩展串行信念更新模型以支持并行迭代。
- 能恢复文献中合理的性质，避免可疑结论。
- 适合研究信念更新逻辑与知识表示的学者。
尽管已有研究探讨单步并行信念更新的约束，但关于如何将其扩展到迭代情形的工作仍很少。Delgrande与Jin最近提出了一系列相关的合理性公理，其中许多虽合理，却缺乏统一解释。本文借鉴迭代并行收缩的最新成果，提出一种将串行迭代信念更新算子扩展为处理并行变化的通用方法。该方法基于一类称为TeamQueue的顺序聚合器，能有原则地恢复文献中独立合理的性质，同时避免产生较可疑的结果。
原文摘要 · Abstract (English)
Despite efforts to better understand the constraints that operate on single-step parallel (aka "package", "multiple") revision, very little work has been carried out on how to extend the model to the iterated case. A recent paper by Delgrande & Jin outlines a range of relevant rationality postulates. While many of these are plausible, they lack an underlying unifying explanation. We draw on recent work on iterated parallel contraction to offer a general method for extending serial iterated belief revision operators to handle parallel change. This method, based on a family of order aggregators known as TeamQueue aggregators, provides a principled way to recover the independently plausible properties that can be found in the literature, without yielding the more dubious ones.
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