May 2020

AGNN: A Deep Learning Architecture for
Abstract Argumentation Semantics
Dennis CRAANDIJK a,b , Floris BEX b,ca National Police Lab AI, Netherlands Policeb Department of Information and Computing Sciences, Utrecht University
c Institute for Law, Technology and Society, Tilburg University
Keywords. argumentation semantics, neural-symbolic reasoning, graph neuralnetworks

1. Introduction
An increasing amount of research is being directed towards designing deep learningmodels that learn on problems from symbolic domains [2]. One domain in symbolic AI
that is relatively unexplored in this regard is computational argumentation. Much of thetheory in computational argumentation is built on Dung’s [3] work on abstract argumentation frameworks, in which he introduces acceptability semantics that define which setsof arguments (extensions) can be reasonably accepted given an argumentation framework(AF) of arguments and attacks (often represented as a graph). With such semantics, it
can be determined if an argument can be credulously accepted (it is contained in someextensions) or sceptically accepted (it is contained in all extensions).
In [1] we propose AGNN: a deep learning approach that is able to learn to solve
several core problems in abstract argumentation almost perfectly. In this demonstration
we show AGNN’s underlying architecture; what the model learns in order to solve an
argumentation problem and how it differs from symbolic algorithms.

2. Argumentation Graph Neural Network
Most current approaches solve acceptance problems by translating the problem to a symbolic formalism for which a dedicated solver exists. AGNN (argumentation graph neuralnetwork) learns a message passing algorithm to determine sceptical and credulous acceptance and enumerate extensions under 4 well-known argumentation semantics. Experimental results demonstrate that the AGNN can almost perfectly predict acceptability andenumerate extensions, and scales well for larger argumentation frameworks (100-200 arguments).Our learning-based approach to determining argument acceptance shows that
sub-symbolic deep learning techniques can accurately solve a problem that could previously only be solved by sophisticated symbolic solvers. Furthermore, analysing the behaviour of the message-passing algorithm shows that the AGNN learns to adhere to ba-May 2020

tAF F

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AAcbdRRRRRARRAFigure 1. The acceptance predictions AGNN makes after the first three message passing iterations on the AF
F = ({a, b, c, d}, {(a, b), (a, c), (b, c), (b, d), (c, b), (d, c)} with respect to the grounded semantics. The label andcolour of each arguments denote whether the argument is predicted to be A accepted or R rejected where adarker colour indicates a higher confidence prediction (from: [1]).

sic principles of argument semantics as identified in the literature, and in the case of acceptance under the grounded semantics exhibits behaviour similar to a well-establishedsymbolic labelling algorithm [4].

3. Demonstration
We demonstrate our Python implementation of an AGNN and show:• how the AGNN architecture enables learning a neural message passing algorithm• how the parameters of this algorithms can be optimised to predict argument acceptance almost perfectly under different semantics• that the AGNN learns to adhere to basic principles of argument semantics• how the learned algorithm differs from symbolic algorithms
We do so by graphically demonstrating AGNN’s behaviour on an AF (cf. Figure 1).

References[1]Dennis Craandijk and Floris Bex. Deep learning for abstract argumentation semantics. In Proceedings ofthe 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), 2020. in press.
[2] Artur S. d’Avila Garcez, Tarek R. Besold, Luc De Raedt, Peter Földiák, Pascal Hitzler, Thomas Icard,Kai-Uwe Kühnberger, Luı́s C. Lamb, Risto Miikkulainen, and Daniel L. Silver. Neural-symbolic learningand reasoning: Contributions and challenges. In Proceedings of the 2015 AAAI Spring Symposia, Palo
Alto, 2015. Stanford University.
[3] Phan Minh Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning,
logic programming and n-person games. Artificial Intelligence, 77(2):321–358, 1995.
[4] Sanjay Modgil and Martin Caminada. Proof theories and algorithms for abstract argumentation frameworks. In Iyad Rahwan and Guillermo R Simari, editors, Argumentation in Artificial Intelligence, pages
105–129. 2009.