Challenges in Explainability and Knowledge Extraction

Daiki Kimura, Tsunehiko Tanaka, Michiaki Tatsubori, Asim Munawar

Abstract: Since text-based games are one of the most challenging problems and require to have an understanding of language and decision-making in a complex environment, there are many studies about applying deep reinforcement learning methods. However, these deep models are often black-box which means the human operator cannot understand the trained rules. And the models use external knowledge such as common-sense knowledge from well-designed data. In this paper, we explain some challenges in solving these problems in text-based games.

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