用模糊逻辑让程序理解‘高’‘低’等模糊概念，结合数值数据与人类推理。
Applying Answer Set Programming with Fuzzy Membership Functions: a Case Study
- 引入模糊隶属函数扩展答案集编程，支持非精确的语义推理。
- 可融合机器学习输出与专家经验，实现上下文相关的定性判断。
- 适合需要主观判断与多源信息整合的智能系统开发。
人类推理常依赖于'高'、'低'、'贵'、'便宜'等语言标签表达的定性概念，其含义取决于上下文且通常模糊，尽管根植于数值数据。本文探索了一种基于模糊逻辑的定性扩展答案集编程（ASP），以弥合数值信息与定性推理之间的鸿沟。该语言在另一项工作中正式提出，提供了一个避免刚性阈值的严谨框架，支持在模糊情境下的稳健推理。以典型应用场景为例，展示如何将机器学习模型输出等数值输入与对定性标签的符号推理相结合。关键特性包括基于学习的隶属函数和语义增强谓词，使专家知识、上下文因素与主观解释能在统一的声明式设置中融合。
原文摘要 · Abstract (English)
Human reasoning often operates through qualitative concepts expressed by linguistic labels such as high, low, expensive, or cheap, whose interpretation depends on context and is usually vague, despite being rooted in numerical data. This paper explores a novel fuzzy-logic-based qualitative extension of Answer Set Programming (ASP) to bridge numerical information and qualitative reasoning. The underlying language, formally introduced in a separate work, provides a principled framework that avoids rigid thresholds and supports robust reasoning under vagueness. Focusing on a representative use case, we illustrate how the framework integrates numerically grounded inputs (such as outputs of machine learning models) with symbolic reasoning over qualitative labels. Key features, including learning-based membership functions and semantically enriched predicates, enable the combination of expert knowledge, contextual factors, and subjective interpretations within a unified declarative setting.
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