Design Adaptive AI for RTS Game by Learning Player's Build Order
Abstract
Digital games have proven to be valuable simulation environments for plan and goal recognition. Though, goal recognition is a hard problem, especially in the field of digital games where players unintentionally achieve goals through exploratory actions, abandon goals with little warning, or adopt new goals based upon recent or prior events. In this paper, a method using simulation and bayesian programming to infer the player's strategy in a Real-Time-Strategy game (RTS) is described, as well as how we could use it to make more adaptive AI for this kind of game and thus make more challenging and entertaining games for the players.
Cite
Text
Lorthioir and Inoue. "Design Adaptive AI for RTS Game by Learning Player's Build Order." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/737Markdown
[Lorthioir and Inoue. "Design Adaptive AI for RTS Game by Learning Player's Build Order." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/lorthioir2020ijcai-design/) doi:10.24963/IJCAI.2020/737BibTeX
@inproceedings{lorthioir2020ijcai-design,
title = {{Design Adaptive AI for RTS Game by Learning Player's Build Order}},
author = {Lorthioir, Guillaume and Inoue, Katsumi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {5194-5195},
doi = {10.24963/IJCAI.2020/737},
url = {https://mlanthology.org/ijcai/2020/lorthioir2020ijcai-design/}
}