  We propose a novel method for automatic reasoning on knowledge graphs based
on debate dynamics. The main idea is to frame the task of triple classification
as a debate game between two reinforcement learning agents which extract
arguments -- paths in the knowledge graph -- with the goal to promote the fact
being true (thesis) or the fact being false (antithesis), respectively. Based
on these arguments, a binary classifier, called the judge, decides whether the
fact is true or false. The two agents can be considered as sparse, adversarial
feature generators that present interpretable evidence for either the thesis or
the antithesis. In contrast to other black-box methods, the arguments allow
users to get an understanding of the decision of the judge. Since the focus of
this work is to create an explainable method that maintains a competitive
predictive accuracy, we benchmark our method on the triple classification and
link prediction task. Thereby, we find that our method outperforms several
baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also
conduct a survey and find that the extracted arguments are informative for
users.
