Information Set Generation in Partially Observable Games
Abstract
We address the problem of making single-point decisions in large partially observable games, where players interleave observation, deliberation, and action. We present information set generation as a key operation needed to reason about games in this way. We show how this operation can be used to implement an existing decision-making algorithm. We develop a constraint satisfaction algorithm for performing information set generation and show that it scales better than the existing depth-first search approach on multiple non-trivial games.
Cite
Text
Richards and Amir. "Information Set Generation in Partially Observable Games." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8146Markdown
[Richards and Amir. "Information Set Generation in Partially Observable Games." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/richards2012aaai-information/) doi:10.1609/AAAI.V26I1.8146BibTeX
@inproceedings{richards2012aaai-information,
title = {{Information Set Generation in Partially Observable Games}},
author = {Richards, Mark and Amir, Eyal},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2012},
pages = {549-555},
doi = {10.1609/AAAI.V26I1.8146},
url = {https://mlanthology.org/aaai/2012/richards2012aaai-information/}
}