Details

Autor(en) / Beteiligte

• Isobe, Takeru
• Inoue, Katsumi

Titel

Learning Strategies of Inductive Logic Programming Using Reinforcement Learning

Ist Teil von

• Inductive Logic Programming, 2023, Vol.14363, p.46-61

Ort / Verlag

Switzerland: Springer International Publishing AG

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 show some empirical results.