Enforcement Heuristics for Argumentation with Deep Reinforcement Learning

In this paper, we present a learning-based approach to the symbolic reasoning problem of dynamic argumentation, where the knowledge about attacks between arguments is incomplete or evolving. Specifically, we employ deep reinforcement learning to learn which attack relations between arguments should be added or deleted in order to enforce the acceptability of (a set of) arguments. We show that our Graph Neural Network (GNN) architecture EGNN can learn a near optimal enforcement heuristic for all common argumentfixed enforcement problems, including problems for which no other (symbolic) solvers exist. We demonstrate that EGNN outperforms other GNN baselines and on enforcement problems with high computational complexity performs better than state-of-the-art symbolic solvers with respect to efficiency. Thus, we show our neuro-symbolic approach is able to learn heuristics without the expert knowledge of a human designer and offers a valid alternative to symbolic solvers. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation.