论文标题
抽象论证语义的深度学习
Deep Learning for Abstract Argumentation Semantics
论文作者
论文摘要
在本文中，我们提出了一种基于学习的方法来确定在几种抽象论证语义下接受论证的接受。更具体地说，我们提出了一个论证图神经网络（AGNN），该论证可以学习一种消息通讯算法，以预测被接受的参数的可能性。实验结果表明，对于更大的论证框架，AGNN几乎可以完美地预测不同语义和尺度下的可接受性。此外，分析消息通话算法的行为表明，AGNN学会遵守文献中确定的参数语义的基本原理，因此可以培训以预测不同语义下的扩展 - 我们显示如何通过使用AGNN来指导基本搜索来指导多次扩展语义。我们在https://github.com/denniscraandijk/dl-abstract-argumentation上发布代码
In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics. More specifically, we propose an argumentation graph neural network (AGNN) that learns a message-passing algorithm to predict the likelihood of an argument being accepted. The experimental results demonstrate that the AGNN can almost perfectly predict the acceptability under different semantics and scales well for larger argumentation frameworks. Furthermore, analysing the behaviour of the message-passing algorithm shows that the AGNN learns to adhere to basic principles of argument semantics as identified in the literature, and can thus be trained to predict extensions under the different semantics - we show how the latter can be done for multi-extension semantics by using AGNNs to guide a basic search. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation
