Towards the Automatic Synthesis of Interpretable Chess Tactics
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...
| Authors | Abhijeet Krishnan, Chris Martens |
|---|---|
| Primary category | cs.AI |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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