Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study

DOI10.1007/978-3-030-35514-2_3zbMath1440.68268OpenAlexW2994561262MaRDI QIDQ3297798

Isabelle Kuhlmann, Matthias Thimm

Publication date: 20 July 2020

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-35514-2_3

zbMATH Keywords

neural networkreasoningabstract argumentation

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Logic in artificial intelligence (68T27) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

Shedding new light on the foundations of abstract argumentation: modularization and weak admissibility

Uses Software

• Isabelle
• TensorFlow
• ImageNet
• CoQuiAAS
• AlexNet

Cites Work

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• Wavelets on graphs via spectral graph theory
• The first international competition on computational models of argumentation: results and analysis
• On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and \(n\)-person games
• Design and results of the second international competition on computational models of argumentation
• Statistical mechanics of complex networks
• New stochastic local search approaches for computing preferred extensions of abstract argumentation
• Collective dynamics of ‘small-world’ networks

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