Interpretable knowledge recommendation for intelligent process planning with graph embedded deep reinforcement learning

Abstract

In the context of Industry 4.0, knowledge recommendation serves as the basis for intelligent process planning. However, the limited interpretability of knowledge recommendation systems make it challenging for users to understand and trust the recommendation process. Consequently, this paper defines an interpretable knowledge recommendation process (iKRP) task that transforms the knowledge recommendation process into a sequential decision-making task through deep reinforcement learning (DRL). It then generates relational paths to the answers based on the topic entities within the knowledge graph. To improve the interpretability of the recommended process knowledge, the following research approaches are proposed: (1) a framework for recommending sequences of process decision knowledge; (2) a TransEx knowledge graph embedding model that integrates attention mechanisms and complex-valued embeddings, with the accuracy improvements of 5.56 % over baseline method; (3) a process knowledge recommendation network based on DRL through the asynchronous superior actor-critic algorithm to achieve interpretability; (4) enhanced interpretability of the recommended process knowledge via the presentation of clear decision paths. Finally, the validity and reliability of the proposed method are demonstrated through application cases, which achieve a final accuracy rate of 0.8148.