用可解释的辩论机制做分类，让模型像人一样思考。
Neuro-Argumentative Learning with Case-Based Reasoning
- 用案例当论点，通过神经符号学习辩论过程。
- 多分类性能媲美神经网络，且能给出不确定度。
- 适合需要透明决策的场景，如医疗、司法辅助。
我们提出渐进式抽象案例推理论证（Gradual AA-CBR），一种数据驱动的神经符号分类模型。模型通过学习神经特征提取器与论证结构共同决定输出。每个论点对应训练数据中的一个案例，支持或攻击其他案例，其强度和关系通过梯度方法学习。该论证结构提供人类对齐的推理过程，提升模型可解释性。相较于现有纯符号版本（AA-CBR），Gradual AA-CBR 可实现多分类、自动学习特征与数据点重要性、为结果分配不确定性值、使用全部数据点，且无需二元特征。实验表明，其性能接近神经网络，显著优于现有AA-CBR方法。
原文摘要 · Abstract (English)
We introduce Gradual Abstract Argumentation for Case-Based Reasoning (Gradual AA-CBR), a data-driven, neurosymbolic classification model in which the outcome is determined by an argumentation debate structure that is learned simultaneously with neural-based feature extractors. Each argument in the debate is an observed case from the training data, favouring their labelling. Cases attack or support those with opposing or agreeing labellings, with the strength of each argument and relationship learned through gradient-based methods. This argumentation debate structure provides human-aligned reasoning, improving model interpretability compared to traditional neural networks (NNs). Unlike the existing purely symbolic variant, Abstract Argumentation for Case-Based Reasoning (AA-CBR), Gradual AA-CBR is capable of multi-class classification, automatic learning of feature and data point importance, assigning uncertainty values to outcomes, using all available data points, and does not require binary features. We show that Gradual AA-CBR performs comparably to NNs whilst significantly outperforming existing AA-CBR formulations.
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