Boolean Games: Inferring Agents' Goals Using Taxation Queries
Abstract
In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.
Cite
Text
Adiga et al. "Boolean Games: Inferring Agents' Goals Using Taxation Queries." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/220Markdown
[Adiga et al. "Boolean Games: Inferring Agents' Goals Using Taxation Queries." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/adiga2020ijcai-boolean/) doi:10.24963/IJCAI.2020/220BibTeX
@inproceedings{adiga2020ijcai-boolean,
title = {{Boolean Games: Inferring Agents' Goals Using Taxation Queries}},
author = {Adiga, Abhijin and Kraus, Sarit and Maksimov, Oleg and Ravi, S. S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {1585-1591},
doi = {10.24963/IJCAI.2020/220},
url = {https://mlanthology.org/ijcai/2020/adiga2020ijcai-boolean/}
}