标题： 基于逻辑的可解释性的神经网络编码比较 显示英文标题

标题： Comparing Neural Network Encodings for Logic-based Explainability

摘要： 为人工神经网络（ANNs）的输出提供解释在许多背景下至关重要，例如关键系统、数据保护法规以及处理对抗样本。 基于逻辑的方法可以提供具有正确性保证的解释，但面临可扩展性挑战。 由于这些问题，有必要比较ANN转换为逻辑约束的不同编码方式，这些编码方式用于基于逻辑的可解释性方法中。 本研究对比了两种ANN的编码方式：一种已在文献中用于提供解释，而另一种将被调整以适应我们的可解释性背景。 此外，第二种编码方式使用更少的变量和约束，因此可能提高效率。 实验结果显示，两种编码方式在计算解释时的运行时间相似，但经过调整的编码方式在构建逻辑约束方面最多提高了18%，在总体运行时间上最多提高了16%。 显示英文摘要

摘要： Providing explanations for the outputs of artificial neural networks (ANNs) is crucial in many contexts, such as critical systems, data protection laws and handling adversarial examples. Logic-based methods can offer explanations with correctness guarantees, but face scalability challenges. Due to these issues, it is necessary to compare different encodings of ANNs into logical constraints, which are used in logic-based explainability. This work compares two encodings of ANNs: one has been used in the literature to provide explanations, while the other will be adapted for our context of explainability. Additionally, the second encoding uses fewer variables and constraints, thus, potentially enhancing efficiency. Experiments showed similar running times for computing explanations, but the adapted encoding performed up to 18\% better in building logical constraints and up to 16\% better in overall time.