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Papers

Deep Learning for Abstract Argumentation Semantics

2020-07-15 · Dennis Craandijk, Floris Bex

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

📄 PDF Abstract BibTeX arXiv:2007.07629

Code (1)

DennisCraandijk/DL-Abstract-Argumentation 공식 구현 pytorch

Tasks

Abstract ArgumentationDeep LearningGraph Neural NetworkNode Classification

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음
GCN A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of [convolutional neural…
MPNN There are at least eight notable examples of models from the literature that can be described using the Message Passing Neural Networks (MPNN) framework. For simplicity we…

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