Efficient Object Search in Game Maps

Abstract

Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.

Cite

Text

Du et al. "Efficient Object Search in Game Maps." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/618

Markdown

[Du et al. "Efficient Object Search in Game Maps." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/du2023ijcai-efficient/) doi:10.24963/IJCAI.2023/618

BibTeX

@inproceedings{du2023ijcai-efficient,
  title     = {{Efficient Object Search in Game Maps}},
  author    = {Du, Jinchun and Shen, Bojie and Zhao, Shizhe and Cheema, Muhammad Aamir and Toosi, Adel Nadjaran},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {5567-5576},
  doi       = {10.24963/IJCAI.2023/618},
  url       = {https://mlanthology.org/ijcai/2023/du2023ijcai-efficient/}
}