用解释驱动规则优化，提升知识系统决策质量
Explainability-Driven Quality Assessment for Rule-Based Systems
- 通过四种解释类型实现规则的自动化诊断与迭代
- 结合人工反馈优化规则逻辑，解决不一致与阈值问题
- 适合需要可解释性的金融等高风险领域应用
本文提出一种基于数据驱动的解释框架，用于提升基于规则的知识推理系统的规则质量。传统规则归纳依赖大量人工标注和数据学习，而该框架提供替代方案：通过生成规则推理的解释，并借助人工理解来改进现有规则。框架整合了四种互补的解释类型——基于痕迹的、情境的、对比的和反事实的，为规则调试、验证与优化提供多维度视角。通过将可解释性嵌入推理架构，该方法使知识工程师能够识别不一致性、优化阈值，并保障决策过程的公平性、透明性与可解释性。其有效性在金融领域的实际案例中得到验证。
原文摘要 · Abstract (English)
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights. The traditional method for rule induction from data typically requires labor-intensive labeling and data-driven learning. This framework provides an alternative and instead allows for the data-driven refinement of existing rules: it generates explanations of rule inferences and leverages human interpretation to refine rules. It leverages four complementary explanation types: trace-based, contextual, contrastive, and counterfactual, providing diverse perspectives for debugging, validating, and ultimately refining rules. By embedding explainability into the reasoning architecture, the framework enables knowledge engineers to address inconsistencies, optimize thresholds, and ensure fairness, transparency, and interpretability in decision-making processes. Its practicality is demonstrated through a use case in finance.
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