Asymmetric Co-Evolution for Imperfect-Information Zero-Sum Games
Abstract
We present an asymmetric co-evolutionary learning algorithm for imperfect-information zero-sum games. This algorithm is designed so that the fitness of the individual agents is calculated in a way that is compatible with the goal of game-theoretic optimality. This compatibility has been somewhat lacking in previous co-evolutionary approaches, as these have often depended on unwarranted assumptions about the absolute and relative strength of players. Our algorithm design is tested on a game for which the optimal strategy is known, and is seen to work well.
Cite
Text
Halck and Dahl. "Asymmetric Co-Evolution for Imperfect-Information Zero-Sum Games." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_18Markdown
[Halck and Dahl. "Asymmetric Co-Evolution for Imperfect-Information Zero-Sum Games." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/halck2000ecml-asymmetric/) doi:10.1007/3-540-45164-1_18BibTeX
@inproceedings{halck2000ecml-asymmetric,
title = {{Asymmetric Co-Evolution for Imperfect-Information Zero-Sum Games}},
author = {Halck, Ole Martin and Dahl, Fredrik A.},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {171-182},
doi = {10.1007/3-540-45164-1_18},
url = {https://mlanthology.org/ecmlpkdd/2000/halck2000ecml-asymmetric/}
}