用代数表达式实现可交互的模型解释，支持多层抽象推理
ReasonX: Declarative Reasoning on Explanations
- 基于线性约束代数构建查询系统，支持用户自定义背景知识
- 结合MILP求解器分析事实与反事实样本特征，实现精准解释
- 适合需要可解释决策过程的研究者和高风险场景应用
解释复杂的机器学习模型已成为重要挑战。现有AI可解释性（XAI）方法存在抽象不足、用户交互有限、符号知识整合不够等问题。我们提出ReasonX，一种基于线性约束理论中算子闭包的表达式（或查询）工具。ReasonX为决策树提供声明式且可交互的解释，这些决策树可代表待分析的模型，也可作为任意黑盒预测器的全局或局部代理模型。用户可将背景知识或常识表示为线性约束，实现从完全指定实例到部分约束实例的多层级抽象推理。ReasonX利用混合整数线性规划（MILP）对事实与反事实实例的特征进行推理。本文展示了ReasonX的架构，包括贴近用户的Python层与实现查询代数元解释器的约束逻辑编程（CLP）层。通过定性示例和定量实验对比，验证了ReasonX的能力。
原文摘要 · Abstract (English)
Explaining opaque Machine Learning (ML) models has become an increasingly important challenge. However, current eXplanation in AI (XAI) methods suffer several shortcomings, including insufficient abstraction, limited user interactivity, and inadequate integration of symbolic knowledge. We propose ReasonX, an explanation tool based on expressions (or, queries) in a closed algebra of operators over theories of linear constraints. ReasonX provides declarative and interactive explanations for decision trees, which may represent the ML models under analysis or serve as global or local surrogate models for any black-box predictor. Users can express background or common sense knowledge as linear constraints. This allows for reasoning at multiple levels of abstraction, ranging from fully specified examples to under-specified or partially constrained ones. ReasonX leverages Mixed-Integer Linear Programming (MILP) to reason over the features of factual and contrastive instances. We present here the architecture of ReasonX, which consists of a Python layer, closer to the user, and a Constraint Logic Programming (CLP) layer, which implements a meta-interpreter of the query algebra. The capabilities of ReasonX are demonstrated through qualitative examples, and compared to other XAI tools through quantitative experiments.
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