# Strategic Features for General Games

arXiv: https://arxiv.org/abs/2101.00843 · HTML: https://mlsanity.com/paper/2101.00843/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 27 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 4 of 4)

- [General Board Game Concepts](https://mlsanity.com/paper/2107.01078/) (2021)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [Automatic Generation of Board Game Manuals](https://mlsanity.com/paper/2109.09507/) (2021)
- [Learning to Play: Reinforcement Learning and Games](https://mlsanity.com/paper/2610.17391/) (2026)

## Abstract (arXiv)

This short paper describes an ongoing research project that requires the automated self-play learning and evaluation of a large number of board games in digital form. We describe the approach we are taking to determine relevant features, for biasing MCTS playouts for arbitrary games played on arbitrary geometries. Benefits of our approach include efficient implementation, the potential to transfer learnt knowledge to new contexts, and the potential to explain strategic knowledge embedded in features in human-comprehensible terms.