Strategic Features for General Games
Artificial Intelligence
2021-01-05 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2101.00843,
title = {Strategic Features for General Games},
author = {Cameron Browne and Dennis J. N. J. Soemers and Eric Piette},
journal= {arXiv preprint arXiv:2101.00843},
year = {2021}
}
Comments
Paper exactly as it appeared at KEG Workshop held at AAAI 2019