提出一种通用的上下文定位方法，解决知识系统中评分聚合的稳定性问题。
Context Localization for Generalized Level-Based Evaluation in Knowledge-Based Systems
- 通过上下文交集实现评分过滤与上下文定位的一致性
- 证明了单调性与排除外部支持的结构性条件是关键
- 适用于证据选择、非加性评估等场景，适合知识推理研究者
本文研究知识系统中广义层级评估的上下文定位问题。框架建模了在事实、规则、案例、标准或证据单元上定义的结构化非负评分，通过在可接受知识上下文中进行条件聚合测试来评估。广义层级度量在所有聚合支持达到预定水平的上下文中，最大化单调集合函数。我们刻画了何时通过上下文 $B$ 进行评分过滤等价于将可接受上下文限制为与 $B$ 的交集。主定理表明，该一致性对所有单调集合函数成立当且仅当满足两个结构条件：对上下文的单调性，以及排除 $B$ 外部存在正局部支持的归约性质。我们分析了点对点和块生成机制如何产生归约性质，将结果扩展到参数化系统，并将其解释为上下文依赖证据选择、非加性支持评估与层级知识聚合的稳定性准则。
原文摘要 · Abstract (English)
We study context localization for generalized level-based evaluation in knowledge-based systems. The framework models situations where a structured nonnegative score, defined on facts, rules, cases, criteria or evidence units, is evaluated through conditional aggregation tests on admissible knowledge contexts. The generalized level measure maximizes a monotone set function over all contexts whose aggregated support reaches a prescribed level. We characterize when filtering the score by a context $B$ is equivalent to localizing the admissible contexts by intersection with $B$. The main theorem shows that this consistency holds for all monotone set functions if and only if two structural conditions are satisfied: monotonicity with respect to contexts and a reduction property excluding positive localized support outside $B$. We analyze pointwise and block-generated mechanisms producing the reduction property, extend the result to parameterized systems, and interpret it as a stability criterion for context-dependent evidence selection, non-additive support evaluation and level-based knowledge aggregation.
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