发表机构
Institute of Mathematics, Faculty of Science, Pavol Jozef Šafárik University（帕沃尔·约瑟夫·沙法里克大学理学院数学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对知识系统的广义水平评估，通过刻画上下文过滤与定位的一致性条件，为证据选择、支持评估及知识聚合提供稳定性准则。
AI 中文摘要
我们研究知识系统中广义水平评估的上下文定位问题。该框架对以下场景进行建模：基于事实、规则、案例、标准或证据单元定义的结构化非负得分，通过对可允许知识上下文的条件聚合测试进行评估。广义水平测度在所有聚合支持达到规定水平的上下文上最大化单调集函数。我们刻画了将得分按上下文B过滤，等价于通过与B相交来定位可允许上下文的条件。主定理表明，当且仅当满足两个结构条件时，该一致性对所有单调集函数成立：一是关于上下文的单调性，二是排除B之外正局部支持的归约性质。我们分析了产生归约性质的逐点和块生成机制，将结果扩展至参数化系统，并将其解释为上下文相关证据选择、非加性支持评估及基于水平的知识聚合的稳定性准则。
英文摘要
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.
CommentsAuthor preprint of the article published in Knowledge-Based Systems (2026)
Journal refKnowledge-Based Systems 351 (2026), 116832
DOI：10.1016/j.knosys.2026.116832
