Learning Strategies of Inductive Logic Programming Using Reinforcement Learning

Learning settings are crucial for most Inductive Logic Programming (ILP) systems to learn efficiently. Hypothesis spaces can be huge, and ILP systems take a long time to output solutions or even cannot terminate within time limits. Therefore, users must set suitable learning settings for each ILP task to bring the best performance of the system. However, most users struggle to set appropriate settings for the task they see for the first time. In this paper, we propose a method to make an ILP system more adaptable to tasks with weak learning biases. In particular, we attempt to learn efficient strategies for an ILP system using reinforcement learning (RL). We use Popper, a state-of-the-art ILP system that implements the concept of learning from failures (LFF). We introduce RL-Popper, which divides the hypothesis space into subspaces more minutely than Popper. RL is used to learn the efficient search order of the divided spaces. We provide the details of RL-Popper and showsome empirical results.