An Expert-Level Card Playing Agent Based on a Variant of Perfect Information Monte Carlo Sampling
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 125-130.
Abstract
Despite some success of Perfect Information Monte Carlo Sampling (PIMC) in imperfect information games in the past, it has been eclipsed by other approaches in recent years. Standard PIMC has well-known shortcomings in the accuracy of its decisions, but has the advantage of being simple, fast, robust and scalable, making it well-suited for imperfect information games with large state-spaces. We propose Presumed Value PIMC resolving the problem of overestimation of opponent's knowledge of hidden information in future game states. The resulting AI agent was tested against human experts in Schnapsen, a Central European 2-player trick-taking card game, and performs above human expert-level.
@inproceedings{ijcai2015p25,
title = {An Expert-Level Card Playing Agent Based on a Variant of Perfect Information Monte Carlo Sampling},
author = {Wisser, Florian},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {125--130},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/25},
}
TY - CPAPER TI - An Expert-Level Card Playing Agent Based on a Variant of Perfect Information Monte Carlo Sampling AU - Wisser, Florian T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 125 EP - 130 UR - https://ijcai.org/proceedings/2015/25 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
