Towards the Automatic Synthesis of Interpretable Chess Tactics

2026-10-05

AI Summary

This study addresses the lack of interpretability in reinforcement learning-based chess agents by extracting human-understandable tactical knowledge from black-box models. Methodologically, it innovatively integrates chess tactical priors to construct a symbolic sub-policy model and employs the PAL inductive logic programming system to extract board patterns. Furthermore, a novel divergence metric and computational evaluation scheme are proposed to validate the model's effectiveness. The results demonstrate that this approach successfully derives a set of tactically viable strategies whose move recommendations approximate those of novice human players. By maintaining decision interpretability, this work achieves an effective integration of symbolic AI and reinforcement learning.

Abstract

State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These agents do not simply take advantage of their digital hardware in being able to react and calculate faster than humans, but employ better strategies that lead to more victories. Interpreting these strategies would give human players valuable insight into how to improve their play. In this preliminary work, we propose a symbolic sub-policy model for playing chess. Inspired by chess tactics, our model attempts to incorporate domain knowledge to improve interpretability. We adapt patterns learned by an inductive logic programming system called PAL to derive our model. We contribute a divergence metric to evaluate our model against a random baseline, and find a set of tactics that is able to suggest moves of similar playing strength to a human beginner. Finally, we propose a computational evaluation scheme for the model by augmenting an off-the-shelf engine with it.

Problem

• Reinforcement Learning
• Interpretability
• Chess Tactics
• Symbolic Sub-policy
• Inductive Logic Programming

Innovation

• Interpretable Reinforcement Learning
• Symbolic Sub-policy Model
• Inductive Logic Programming
• Chess Tactics
• Divergence Metric